News

Wikipedia Page on Inductive Programming

José Hernández-Orallo and Ute Schmid created Wikipedia articles for Inductive Programming and Inductive Functional Programming.

15.01.2015
Dagstuhl Seminar "Approaches and Applications of Inductive Programming"

José Hernández-Orallo (Polytechnic University of Valencia, ES), Stephen H. Muggleton (Imperial College London, GB), Ute Schmid (Universität Bamberg, DE) and Benjamin Zorn (Microsoft Research - Redmond, US) organize Dagstuhl Seminar 15442 "Approaches and Applications of Inductive Programming" scheduled for October 25 to 30, 2015.

The seminar is a continuation of the AAIP workshop series.

Please visit the AAIP 15 Homepage.

07.10.2014
Report of Dagstuhl Seminar

We're pleased to inform you that the report of Dagstuhl Seminar 13502 is now published as part of the periodical Dagstuhl Reports.

The report is available online at the DROPS Server.

31.03.2014
Dagstuhl Seminar "Approaches and Applications of Inductive Programming"

Ute Schmid (University of Bamberg), Emanuel Kitzelmann (University of Duisburg-Essen), Sumit Gulwani (Microsoft Research) and Marcus Hutter (Austrian National University) organize Dagstuhl Seminar 13502 "Approaches and Applications of Inductive Programming" scheduled for Monday, December 09 to December 11, 2013. The seminar is a continuation of the AAIP workshop series.

Please visit the AAIP 13 Homepage.

15.12.2012
4th Workshop AAIP 2011

AAIP 2011 Homepage

Ute Schmid and Emanuel Kitzelmann organize the 4th Workshop on Approaches and Applications of Inductive Programming. It will take place on July 19, 2011, in Odense, Denmark. Co-located events are the 13th International ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming (PPDP 2011) and the 21st International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2011).

Details can be found on the AAIP 2011 Homepage.

12.01.2011

Publications

Here's a comprehensive list of inductive programming publications.
If you wish your publications to be added, or some important papers are missing, then please send a message with the BibTex entries to the admin.

 
Ramiro Aguilar, Luis Alonso, Vivian Lòpez, and María N. Moreno. Incremental discovery of sequential patterns for grammatical inference. In Emanuel Kitzelmann, Roland J. Olsson, and Ute Schmid, editors, AAIP'05: Proceedings of the 1st Workshop on Approaches and Applications of Inductive Programming (Bonn, Germany, Aug.7, 2005), pages 59-67, 2005. Work in Progress Reports.
@inproceedings{aguilar_ea:2005,
  author = {Ramiro Aguilar and Luis Alonso and Vivian L\`opez and
		  Mar\'ia N. Moreno},
  title = {Incremental discovery of sequential patterns for
		  grammatical inference},
  editor = {Emanuel Kitzelmann and Roland J. Olsson and Ute Schmid},
  booktitle = {{AAIP'05}: Proceedings of the 1st Workshop on Approaches
		  and Applications of Inductive Programming (Bonn, Germany,
		  Aug.\,7, 2005)},
  year = 2005,
  pages = {59--67},
  note = {Work in Progress Reports},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip05/proceedings/artirami.pdf}
}
 
David W. Aha. Case-based learning algorithms. In I. Bareiss, R. Lewis, and S. Gravitis, editors, Proceedings of the DARPA Case-Based Reasoning Workshop (Pensacola Beach, Florida, May-June, 1989), volume 1, pages 147-158, Washington, D. C., 1991. Morgan Kaufmann.
@inproceedings{aha:1991,
  author = {Aha, David W.},
  title = {Case-Based Learning Algorithms},
  editor = {I. Bareiss and R. Lewis and S. Gravitis},
  booktitle = {Proceedings of the {DARPA} Case-Based Reasoning Workshop
		  (Pensacola Beach, Florida, May--June, 1989)},
  year = 1991,
  volume = 1,
  pages = {147--158},
  address = {Washington, D. C.},
  publisher = {Morgan Kaufmann}
}
 
David W. Aha, Charles X. Ling, Stan Matwin, and S. Lapointe. Learning singly-recursive relations from small datasets. In Ruzena Bajcsy, editor, IJCAI'93: Proceedings of the 13th International Joint Conference on Artificial Intelligence (Chambéry, France, Aug.28-Sep.3, 1993), pages 47-58. Morgan Kaufmann, 1993.
@inproceedings{aha_ea:1993,
  author = {David W. Aha and Charles X. Ling and Stan Matwin and S.
		  Lapointe},
  title = {Learning Singly-recursive Relations from Small Datasets},
  editor = {Ruzena Bajcsy},
  booktitle = {{IJCAI}'93: Proceedings of the 13th International Joint
		  Conference on Artificial Intelligence (Chamb\'ery, France,
		  Aug.\,28--Sep.\,3, 1993)},
  year = 1993,
  pages = {47--58},
  publisher = {Morgan Kaufmann}
}
 
David W. Aha, Stephane Lapointe, Charles X. Ling, and Stan Matwin. Inverting implication with small training sets. In Francesco Bergadano and Luc De Raedt, editors, Machine Learning: ECML-94. European Conference on Machine Learning, Catania, Italy, April6-8, 1994. Proceedings, volume 784 of Lecture Notes in Computer Science, pages 29-48, Berlin/Heidelberg, 1994. Springer.
@inproceedings{aha_ea:1994,
  author = {David W. Aha and Stephane Lapointe and Charles X. Ling and
		  Stan Matwin},
  title = {Inverting Implication with Small Training Sets},
  editor = {Bergadano, Francesco and De~Raedt, Luc},
  booktitle = {Machine Learning: {ECML-94}. European Conference on
		  Machine Learning, Catania, Italy, April\,6--8, 1994.
		  Proceedings},
  year = 1994,
  series = {Lecture Notes in Computer Science},
  volume = 784,
  pages = {29--48},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  keywords = {CRUSTACEAN; ilp; inductive programming; ip-system; program
		  synthesis; recursion},
  abstract = {We present an algorithm for inducing recursive clauses
		  using inverse implication (rather than inverse resolution)
		  as the underlying generalization method. Our approach
		  applies to a class of logic programs similar to the class
		  of primitive recursive functions. Induction is performed
		  using a small number of positive examples that need not be
		  along the same resolution path. Our algorithm, implemented
		  in a system named CRUSTACEAN, locates matched lists of
		  generating terms that determine the pattern of
		  decomposition exhibited in the (target) recursive clause.
		  Our theoretical analysis defines the class of logic
		  programs for which our approach is complete, described in
		  terms characteristic of other ILP approaches. Our current
		  implementation is considerably faster than previously
		  reported. We present evidence demonstrating that, given
		  randomly selected inputs, increasing the number of positive
		  examples increases accuracy and reduces the number of
		  outputs. We relate our approach to similar recent work on
		  inducing recursive clauses.},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-57868-0},
  url = {http://www.springerlink.com/content/p77364661un577p7/},
  doi = {10.1007/3-540-57868-4_49}
}
 
David W. Aha, S. Lapointe, Charles X. Ling, and Stan Matwin. Learning recursive relations with randomly selected small training sets. In Wiliam W. Cohen and Haym Hirsh, editors, ICML'94: Proceedings of the 11th International Conference on Machine Learning (Rutgers University, New Brunswick, NJ, USA, July10-13, 1994), pages 12-18. Morgan Kaufmann, 1994.
@inproceedings{aha_ea:1994b,
  author = {David W. Aha and S. Lapointe and Charles X. Ling and Stan
		  Matwin},
  title = {Learning Recursive Relations with Randomly Selected Small
		  Training Sets},
  editor = {Wiliam W. Cohen and Haym Hirsh},
  booktitle = {{ICML'94}: Proceedings of the 11th International
		  Conference on Machine Learning (Rutgers University, New
		  Brunswick, NJ, USA, July\,10--13, 1994)},
  year = 1994,
  pages = {12--18},
  publisher = {Morgan Kaufmann},
  isbn = {1-55860-335-2}
}
 
Z. Alexin, T. Gyimothy, and H. Boström. Integrating algorithmic debugging and unfolding transformation in an interactive learner. In W. Wahlster, editor, ECAI'96: Proceedings of the 12th European Conference on Artificial Intelligence (Budapest), pages 403-407, 1996.
@inproceedings{alexin_ea:1996,
  author = {Alexin, Z. and Gyimothy, T. and Bostr\"{o}m, H.},
  title = {Integrating Algorithmic Debugging and Unfolding
		  Transformation in an Interactive Learner},
  editor = {W. Wahlster},
  booktitle = {{ECAI'96}: Proceedings of the 12th European Conference on
		  Artificial Intelligence (Budapest)},
  year = 1996,
  pages = {403--407},
  keywords = {SPECTRE; algorithmic debugging; debugging; ilp; inductive
		  programming; ip-system; program synthesis}
}
 
Saul Amarel. Program synthesis as a theory formulation task: Problem representations and solution methods. In Ryszard S. Michalski, Jaime G. Carbonell, and Tom M. Mitchell, editors, Machine Learning. An Artificial Intelligence Approach, volume 2, chapter 18, pages 499-568. Morgan Kaufmann, Los Altos, CA, 1986.
@incollection{amarel:1986,
  author = {Saul Amarel},
  title = {Program synthesis as a theory formulation task: Problem
		  representations and solution methods},
  editor = {Ryszard S. Michalski and Jaime G. Carbonell and Tom M.
		  Mitchell},
  booktitle = {Machine Learning. An Artificial Intelligence Approach},
  publisher = {Morgan Kaufmann},
  year = 1986,
  volume = 2,
  chapter = 18,
  pages = {499--568},
  address = {Los Altos, CA},
  annote = {ute-inflit}
}
 
John R. Anderson and Christian Lebiere. The Atomic Components of Thought. Lawrence Erlbaum Associates, Inc., 1998.
@book{anderson/lebiere:1998,
  author = {John R. Anderson and Christian Lebiere},
  title = {The Atomic Components of Thought},
  publisher = {Lawrence Erlbaum Associates, Inc.},
  year = 1998,
  keywords = {cognition}
}
 
John R. Anderson, R. Farrell, and R. Sauers. Learning to program in lisp. Cognitive Science, 8:87-129, 1984.
@article{anderson_ea:1984,
  author = {John R. Anderson and R. Farrell and R. Sauers},
  title = {Learning to program in LISP},
  journal = {Cognitive Science},
  year = 1984,
  volume = 8,
  pages = {87--129},
  annote = {ute-psylit}
}
 
John R. Anderson, F. G. Conrad, and A. T. Corbett. Skill acquisition and the LISP tutor. Cognitive Science, 13:467-505, 1989.
@article{anderson_ea:1989,
  author = {John R. Anderson and F. G. Conrad and A. T. Corbett},
  title = {Skill acquisition and the {LISP} tutor},
  journal = {Cognitive Science},
  year = 1989,
  volume = 13,
  pages = {467--505},
  annote = {ute-psylit}
}
 
Dana Angluin and Carl H. Smith. Inductive inference: Theory and methods. Computing Surveys, 15(3):237-269, 1983.
@article{angluin/smith:1983,
  author = {Dana Angluin and Carl H. Smith},
  title = {Inductive Inference: Theory and Methods},
  journal = {Computing Surveys},
  year = 1983,
  volume = 15,
  number = 3,
  pages = {237--269},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  keywords = {1980; Angluin; Smith; article; identification in the
		  limit; inductive inference; overview; survey},
  issn = {0360-0300},
  url = {http://doi.acm.org/10.1145/356914.356918}
}
 
Dana Angluin. Finding patterns common to a set of strings (extended abstract). In STOC'79: Proceedings of the 11th annual ACM symposium on Theory of computing (Atlanta, Georgia, USA, April30-May02, 1979), pages 130-141, New York, NY, USA, 1979. ACM.
@inproceedings{angluin:1979,
  author = {Dana Angluin},
  title = {Finding patterns common to a set of strings (Extended
		  Abstract)},
  booktitle = {{STOC'79}: Proceedings of the 11th annual {ACM} symposium
		  on Theory of computing (Atlanta, Georgia, USA,
		  April\,30--May\,02, 1979)},
  year = 1979,
  pages = {130--141},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  keywords = {1979; Angluin; inproceedings; pattern languages},
  annote = {Finding patterns common to a set of strings (Extended
		  Abstract)},
  url = {http://doi.acm.org/10.1145/800135.804406},
  abstract = {We motivate, formalize, and study a computational problem
		  in concrete inductive inference. A ``pattern'' is defined
		  to be a concatenation of constants and variables, and the
		  language of a pattern is defined to be the set of strings
		  obtained by substituting constant strings for the
		  variables. The problem we consider is, given a set of
		  strings, find a minimal pattern language containing this
		  set. This problem is shown to be effectively solvable in
		  the general case and to lead to correct inference in the
		  limit of the pattern languages. There exists a polynomial
		  time algorithm for it in the restricted case of
		  one-variable patterns. Inference from positive data is
		  re-examined, and a characterization given of when it is
		  possible for a family of recursive languages. Various
		  collateral results about patterns and pattern languages are
		  obtained. Section 1 is an introduction explaining the
		  context of this work and informally describing the problem
		  formulation. Section 2 is definitions. Section 3 is results
		  concerning patterns and pattern languages. Section 4
		  concerns the abstract question of inference from positive
		  data. Section 5 gives a polynomial time algorithm for
		  finding minimal one-variable pattern languages compatible
		  with a given set of strings. Section 6 contains remarks.}
}
 
Dana Angluin. Queries and concept learning. Machine Learning, 2(4):319-342, April 1988.
@article{angluin:1988,
  author = {Dana Angluin},
  title = {Queries and Concept Learning},
  journal = {Machine Learning},
  year = 1988,
  volume = 2,
  number = 4,
  pages = {319--342},
  month = {April},
  keywords = {Concept learning; supervised learning; queries},
  abstract = {We consider the problem of using queries to learn an
		  unknown concept. Several types of queries are described and
		  studied: membership, equivalence, subset, superset,
		  disjointness, and exhaustiveness queries. Examples are
		  given of efficient learning methods using various subsets
		  of these queries for formal domains, including the regular
		  languages, restricted classes of context-free languages,
		  the pattern languages, and restricted types of
		  prepositional formulas. Some general lower bound techniques
		  are given. Equivalence queries are compared with Valiant's
		  criterion of probably approximately correct identification
		  under random sampling.},
  publisher = {Springer},
  address = {Netherlands},
  issn = {0885-6125 (Print) 1573-0565 (Online)},
  url = {http://www.springerlink.com/content/u228266621966h58/},
  doi = {10.1023/A:1022821128753}
}
 
Dana Angluin. Equivalence queries and approximate fingerprints. In COLT'89: Proceedings of the 2nd Annual Workshop on Computational Learning Theory (Santa Cruz, CA, USA, July31-Aug.2, 1989), pages 134-145, San Francisco, CA, USA, 1990. Morgan Kaufmann.
@inproceedings{angluin:1990,
  author = {Dana Angluin},
  title = {Equivalence Queries and Approximate Fingerprints},
  booktitle = {{COLT'89}: Proceedings of the 2nd Annual Workshop on
		  Computational Learning Theory (Santa Cruz, CA, USA,
		  July\,31--Aug.\,2, 1989)},
  year = 1990,
  pages = {134--145},
  address = {San Francisco, CA, USA},
  publisher = {Morgan Kaufmann},
  isbn = {1-55860-086-8},
  url = {http://portal.acm.org/citation.cfm?id=93351},
  keywords = {induction; learnability; machine learning; pac-learning}
}
 
Yuichiro Anzai and Herbert A. Simon. The theory of learning by doing. Psychological Review, 86(2):124-140, 1979.
@article{anzai/simon:1979,
  author = {Yuichiro Anzai and Herbert A. Simon},
  title = {The Theory of Learning by Doing},
  journal = {Psychological Review},
  year = 1979,
  volume = 86,
  number = 2,
  pages = {124--140},
  publisher = {American Psychological Association},
  keywords = {cognition}
}
 
Y. Anzai and Y. Uesato. Learning recursive procedures by middleschool children. In CogSci'82: Proceedings of the 4th Annual Conference of the Cognitive Science Society, pages 100-102, 1982.
@inproceedings{anzai/uesato:1982,
  author = {Y. Anzai and Y. Uesato},
  title = {Learning recursive procedures by middleschool children},
  booktitle = {{CogSci'82}: Proceedings of the 4th Annual Conference of
		  the Cognitive Science Society},
  year = 1982,
  pages = {100--102}
}
 
Hiroki Arimura. Learning acyclic first-order Horn sentences from entailment. In Ming Li and Akira Maruoka, editors, Algorithmic Learning Theory. 8th International Workshop, ALT'97, Sendai, Japan, Oct.6-8, 1997. Proceedings, volume 1316 of Lecture Notes in Computer Science, pages 432-445, Berlin/Heidelberg, 1997. Springer.
@inproceedings{arimura:1997,
  author = {Hiroki Arimura},
  title = {Learning acyclic first-order {Horn} sentences from
		  entailment},
  editor = {Ming Li and Akira Maruoka},
  booktitle = {Algorithmic Learning Theory. 8th International Workshop,
		  {ALT'97}, Sendai, Japan, Oct.\,6--8, 1997. Proceedings},
  year = 1997,
  series = {Lecture Notes in Computer Science},
  volume = 1316,
  pages = {432--445},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-63577-2},
  url = {http://www.springerlink.com/content/b3161327v1370748/},
  doi = {10.1007/3-540-63577-7_59},
  abstract = {This paper considers the problem of learning an unknown
		  first-order Horn sentenceH*from examples of Horn clauses
		  thatH*either implies or does not imply. Particularly, we
		  deal with a subclass of first-order Horn sentencesACH(k),
		  calledacyclic constrained Horn programs of constant arity
		  k.ACH(k) allows recursions, disjunctive definitions, and
		  the use of function symbols. We present an algorithm that
		  exactly identifies every target Horn programH*in ACH(k) in
		  polynomial time inp,mandnusingO(pmnk+1) entailment
		  equivalence queries andO(pm2n2) request for hint queries,
		  wherepis the number of predicates,mis the number of clauses
		  contained inH*andnis the size of the longest
		  counterexample. This algorithm combines saturation and
		  least general generalization operators to invert resolution
		  steps. Next, using the technique of replacing request for
		  hint queries with entailment membership queries, we have a
		  polynomial time learning algorithm using entailment
		  equivalence and entailment membership queries for a
		  subclass ofACH(k). Finally, we show that any algorithm
		  which learnsACH(k) using entailment equivalence and
		  entailment membership queries makes((mnk) queries, and that
		  the use of entailment cannot be eliminated to learnACH(k)
		  even with both equivalence and membership queries for
		  ground atoms are allowed.}
}
 
A. Armando, A. Smaill, and I. Green. Automatic synthesis of recursive programs: the proof-planning paradigm. In ASE'97: Proceedings of the 12th IEEE Conference on Automated Software Engineering (Lake Tahoe, Nevada, Nov.2-5, 1997), pages 2-9, 1997.
@inproceedings{armando_ea:1997,
  author = {A. Armando and A. Smaill and I. Green},
  title = {Automatic synthesis of recursive programs: the
		  proof-planning paradigm},
  booktitle = {{ASE'97}: Proceedings of the 12th {IEEE} Conference on
		  Automated Software Engineering (Lake Tahoe, Nevada,
		  Nov.\,2--5, 1997)},
  year = 1997,
  pages = {2--9},
  url = {http://dx.doi.org/10.1109/ASE.1997.632818},
  isbn = {0-8186-7961-1},
  keywords = {deductive program synthesis; inproceedings; program
		  synthesis; proof-planning},
  abstract = {We describe a proof plan that characterises a family of
		  proofs corresponding to the synthesis of recursive
		  functional programs. This plan provides a significant
		  degree of automation in the construction of recursive
		  programs from specifications, together with correctness
		  proofs. This plan makes use of meta-variables to allow
		  successive refinement of the identity of unknowns, and so
		  allows the program and the proof to be developed hand in
		  hand. We illustrate the plan with parts of a substantial
		  example-the synthesis of a unification algorithm.}
}
 
Lennart Augustsson. Announcing djinn, version 2004-12-11, a coding wizard, 2005.
@misc{augustsson:2005,
  author = {Lennart Augustsson},
  title = {Announcing Djinn, version 2004-12-11, a coding wizard},
  year = 2005,
  url = {http://permalink.gmane.org/gmane.comp.lang.haskell.general/12747}
}
 
Franz Baader and Tobias Nipkow. Term Rewriting and All That. Cambridge University Press, 1998.
@book{baader/nipkow:1998,
  author = {Franz Baader and Tobias Nipkow},
  title = {Term Rewriting and All That},
  publisher = {Cambridge University Press},
  year = 1998,
  url = {http://www.cambridge.org/us/catalogue/catalogue.asp?isbn=9780521779203},
  keywords = {book; equational logic; term rewriting; universal
		  algebra},
  annote = {the first comprehensive text book on term rewriting},
  isbn = 0521779200
}
 
Y. M. Barzdin', A. N. Brazma, and E. B. Kinber. Inductive synthesis of programs: State of the art, problems, prospects. In Cybernetics and Systems Analysis, volume 23. Springer, 1988. Formerly: Cybernetics. A Translation of Kibernetika i Sistemnyi Analiz.
@incollection{barzdin_ea:1988,
  author = {Barzdin', Y. M. and Br{\=a}zma, A. N. and Kinber, E. B.},
  title = {Inductive Synthesis of Programs: State of the Art,
		  Problems, Prospects},
  booktitle = {Cybernetics and Systems Analysis},
  publisher = {Springer},
  year = 1988,
  volume = 23,
  note = {Formerly: Cybernetics. A Translation of Kibernetika i
		  Sistemnyi Analiz}
}
 
J. M. Barzdinš and U. Sarkans. Incorporating hypothetical knowledge into the process of inductive synthesis. In S. Arikawa and A. K. Sharma, editors, Algorithmic Learning Theory. 7th International Workshop, ALT'96, Sydney, Australia, Oct.23-25, 1996. Proceedings, volume 1160 of Lecture Notes in Computer Science, pages 156-168, Berlin/Heidelberg, 1996. Springer.
@inproceedings{barzdins/sarkans:1996,
  author = {B{\=a}rzdi{\c{n}}{\v{s}}, J. M. and Sarkans, U.},
  title = {Incorporating hypothetical knowledge into the process of
		  inductive synthesis},
  editor = {S. Arikawa and A. K. Sharma},
  booktitle = {Algorithmic Learning Theory. 7th International Workshop,
		  {ALT'96}, Sydney, Australia, Oct.\,23--25, 1996.
		  Proceedings},
  year = 1996,
  series = {Lecture Notes in Computer Science},
  volume = 1160,
  pages = {156--168},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-61863-8},
  url = {http://www.springerlink.com/content/f8l18p3451426727/},
  abstract = {The problem of inductive inference of functions from
		  hypothetical knowledge is investigated in this paper. This
		  type of inductive inference could be regarded as a
		  generalization of synthesis from examples that can be
		  directed not only by input/output examples but also by
		  knowledge of, e. g., functional description's syntactic
		  structure or assumptions about the process of function
		  evaluation. We show that synthesis of this kind is possible
		  by efficiently enumerating the hypothesis space and
		  illustrate it with several examples.},
  doi = {10.1007/3-540-61863-5_43},
  annote = {ute-inflit}
}
 
J. M. Barzdinš, A. N. Brazma, and E. B. Kinber. Models of inductive syntactical synthesis. In Machine Intelligence, volume 12, pages 139-148. Oxford University Press, 1990.
@incollection{barzdins_ea:1990,
  author = {B{\=a}rzdi{\c{n}}{\v{s}}, J. M. and Br{\=a}zma, A. N. and
		  Kinber, E. B.},
  title = {Models of inductive syntactical synthesis},
  booktitle = {Machine Intelligence},
  publisher = {Oxford University Press},
  year = 1990,
  volume = 12,
  pages = {139--148}
}
 
J. M. Barzdinš, G. Barzdinš, K. Apsitis, and U. Sarkans. Towards efficient inductive synthesis of expressions from input/output examples. In K. P. Jandtke, S. Kobayashi, E. Tomita, and T. Yokomori, editors, Algorithmic Learning Theory. 4th International Workshop on Analogical and Inductive Inference, AII '94 5th International Workshop on Algorithmic Learning Theory, ALT'94, Reinhardsbrunn Castle, Germany Oct.10-15, 1994. Proceedings, volume 872 of Lecture Notes in Computer Science, pages 59-72, Berlin/Heidelberg, 1994. Springer.
@inproceedings{barzdins_ea:1994,
  author = {B{\=a}rzdi{\c{n}}{\v{s}}, J. M. and
		  B{\=a}rzdi{\c{n}}{\v{s}}, G. and Aps{\=\i}tis, K. and Sarkans, U.},
  title = {Towards efficient inductive synthesis of expressions from
		  input/output examples},
  editor = {K. P. Jandtke and S. Kobayashi and E. Tomita and T.
		  Yokomori},
  booktitle = {Algorithmic Learning Theory. 4th International Workshop on
		  Analogical and Inductive Inference, AII '94 5th
		  International Workshop on Algorithmic Learning Theory,
		  {ALT'94}, Reinhardsbrunn Castle, Germany Oct.\,10--15,
		  1994. Proceedings},
  year = 1994,
  series = {Lecture Notes in Computer Science},
  volume = 872,
  pages = {59--72},
  address = { Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-58520-6},
  url = {http://www.springerlink.com/content/f2h1q3661h76t808/},
  doi = {10.1007/3-540-58520-6_46},
  annote = {ute-inflit}
}
 
Michael A. Bauer. Programming by examples. Artificial Intelligence, 12:1-21, 1979.
@article{bauer:1979,
  author = {Michael A. Bauer},
  title = {Programming by examples},
  journal = {Artificial Intelligence},
  year = 1979,
  volume = 12,
  pages = {1--21}
}
 
Kent Beck. Test-Driven Development By Example. Addison-Wesley, 2003.
@book{beck:2003,
  author = {Kent Beck},
  title = {Test-Driven Development By Example},
  publisher = {Addison-Wesley},
  year = 2003,
  keywords = {tdd}
}
 
Christoph Beierle. Synthesizing minimal programs from traces of observable behavior. Technical Report SEKI-BN-81-06, SEKI, Institut für Informatik, Universität Bonn, 1981.
@techreport{beierle:1981,
  author = {Christoph Beierle},
  title = {Synthesizing minimal programs from traces of observable
		  behavior},
  institution = {SEKI},
  year = 1981,
  number = {SEKI-BN-81-06},
  address = {Institut f\"ur Informatik, Universit\"at Bonn},
  annote = {ute-inflit}
}
 
Margherita Berardi and Donato Malerba. Learning recursive patterns for biomedical information extraction. In Stephen H. Muggleton, Ramón P. Otero, and Alireza Tamaddoni-Nezhad, editors, Inductive Logic Programming. 16th International Conference, ILP'06, Santiago de Compostela, Spain, Aug.24-27, 2006. Revised Selected Papers, volume 4455 of Lecture Notes in Computer Science, pages 79-93, Berlin/Heidelberg, 2007. Springer.
@inproceedings{berardi/malerba:2007,
  author = {Margherita Berardi and Donato Malerba},
  title = {Learning Recursive Patterns for Biomedical Information
		  Extraction},
  editor = {Stephen H. Muggleton and Ram{\'o}n P. Otero and Alireza
		  Tamaddoni-Nezhad},
  booktitle = {Inductive Logic Programming. 16th International
		  Conference, {ILP'06}, Santiago de Compostela, Spain,
		  Aug.\,24--27, 2006. Revised Selected Papers},
  year = 2007,
  series = {Lecture Notes in Computer Science},
  volume = 4455,
  pages = {79--93},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-73846-6},
  url = {http://www.springerlink.com/content/lw431m8668nu0887/},
  abstract = {Information in text form remains a greatly unexploited
		  source of biological information. Information Extraction
		  (IE) techniques are necessary to map this information into
		  structured representations that allow facts relating
		  domain-relevant entities to be automatically recognized. In
		  biomedical IE tasks, extracting patterns that model
		  implicit relations among entities is particularly important
		  since biological systems intrinsically involve interactions
		  among several entities. In this paper, we resort to an
		  Inductive Logic Programming (ILP) approach for the
		  discovery of mutual recursive patterns from text. Mutual
		  recursion allows dependencies among entities to be explored
		  in data and extraction models to be applied in a
		  context-sensitive mode. In particular, IE models are
		  discovered in form of classification rules encoding the
		  conditions to fill a pre-defined information template. An
		  application to a real-world dataset composed by
		  publications selected to support biologists in the task of
		  automatic annotation of a genomic database is reported.},
  doi = {10.1007/978-3-540-73847-3_15}
}
 
Margherita Berardi, Michelangelo Ceci, Floriana Esposito, and Donato Malerba. Learning logic programs for layout analysis correction. In Nina Mishra Tom Fawcett, editor, ICML'03: Proceedings of the Twentieth International Conference on Machine Learning (Washington D.C., USA, Aug. 21-24, 2003), pages 27-34. AAAI Press, 2003.
@inproceedings{berardi_ea:2003,
  author = {Margherita Berardi and Michelangelo Ceci and Floriana
		  Esposito and Donato Malerba},
  title = {Learning Logic Programs for Layout Analysis Correction},
  editor = {Tom Fawcett, Nina Mishra},
  booktitle = {{ICML'03}: Proceedings of the Twentieth International
		  Conference on Machine Learning (Washington D.C., USA,
		  Aug.\, 21--24, 2003)},
  year = 2003,
  pages = {27--34},
  publisher = {AAAI Press},
  isbn = {1-57735-189-4}
}
 
Margherita Berardi, Antonio Varlaro, and Donato Malerba. On the effect of caching in recursive theory learning. In Rui Camacho, Ross D. King, and Ashwin Srinivasan, editors, Inductive Logic Programming. 14th International Conference, ILP'04, Porto, Portugal, Sept.6-8, 2004. Proceedings, volume 3194 of Lecture Notes in Computer Science, pages 83-90, Berlin/Heidelberg, 2004. Springer.
@inproceedings{berardi_ea:2004,
  author = {Margherita Berardi and Antonio Varlaro and Donato
		  Malerba},
  title = {On the Effect of Caching in Recursive Theory Learning},
  editor = {Rui Camacho and Ross D. King and Ashwin Srinivasan},
  booktitle = {Inductive Logic Programming. 14th International
		  Conference, {ILP'04}, Porto, Portugal, Sept.\,6--8, 2004.
		  Proceedings},
  year = 2004,
  series = {Lecture Notes in Computer Science},
  volume = 3194,
  pages = {83--90},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-22941-4},
  url = {http://springerlink.metapress.com/content/pvyexctkth2c18ny/},
  abstract = {This paper focuses on inductive learning of recursive
		  logical theories from a set of examples. This is a complex
		  task where the learning of one predicate definition should
		  be interleaved with the learning of the other ones in order
		  to discover predicate dependencies. To overcome this
		  problem we propose a variant of the separate-and-conquer
		  strategy based on parallel learning of different predicate
		  definitions. In order to improve its efficiency,
		  optimization techniques are investigated and adopted
		  solutions are described. In particular, two caching
		  strategies have been implemented and tested on document
		  processing datasets. Experimental results are discussed and
		  conclusions are drawn.},
  doi = {10.1007/978-3-540-30109-7_8}
}
 
Henrik Berg, Roland J. Olsson, Per-Olav Rusås, and Morgan Jakobsen. Synthesis of control algorithms for autonomous vehicles through automatic programming. In Haiying Wang, Kay Soon Low, Kexin Wei, and Junqing Sun, editors, ICNC'09: Proceedings of the 5th International Conference on Natural Computation (Tianjin, China, Aug.14-16, 2009), pages 445-453. IEEE Computer Society, 2009.
@inproceedings{berg_ea:2009,
  author = {Henrik Berg and Roland J. Olsson and Per-Olav Rus{\aa}s
		  and Morgan Jakobsen},
  title = {Synthesis of Control Algorithms for Autonomous Vehicles
		  through Automatic Programming},
  editor = {Haiying Wang and Kay Soon Low and Kexin Wei and Junqing
		  Sun},
  booktitle = {{ICNC'09}: Proceedings of the 5th International Conference
		  on Natural Computation (Tianjin, China, Aug.\,14--16,
		  2009)},
  year = 2009,
  pages = {445--453},
  publisher = {IEEE Computer Society},
  keywords = {adate; inductive programming}
}
 
Francesco Bergadano and Daniele Gunetti. An interactive system to learn functional logic programs. In Ruzena Bajcsy, editor, IJCAI'93: Proceedings of the 13th International Joint Conference on Artificial Intelligence (Chambéry, France, Aug.28-Sep.3, 1993). Morgan Kaufmann, 1993.
@inproceedings{bergadano/gunetti:1993,
  author = {Bergadano, Francesco and Gunetti, Daniele},
  title = {An Interactive System to Learn Functional Logic Programs},
  editor = {Ruzena Bajcsy},
  booktitle = {{IJCAI'93}: Proceedings of the 13th International Joint
		  Conference on Artificial Intelligence (Chamb\'ery, France,
		  Aug.\,28--Sep.\,3, 1993)},
  year = 1993,
  publisher = {Morgan Kaufmann},
  keywords = {FILP; iflp; ilp; inductive programming; ip-system; program
		  synthesis; recursion}
}
 
Francesco Bergadano and Daniele Gunetti. Inductive Logic Programming: From Machine Learning to Software Engineering. MIT Press, Cambridge, MA, USA, 1995.
@book{bergadano/gunetti:1995,
  author = {Bergadano, Francesco and Gunetti, Daniele},
  title = {Inductive Logic Programming: From Machine Learning to
		  Software Engineering},
  publisher = {MIT Press},
  year = 1995,
  address = {Cambridge, MA, USA},
  keywords = {book; filp; ilp; induction; inductive programming; machine
		  learning; program synthesis; software engineering},
  isbn = 0262023938,
  url = {http://portal.acm.org/citation.cfm?id=546596#}
}
 
W. Bibel and A. W. Biermann. Special issue: Automatic programming, foreword of the guest editors. Journal of Symbolic Computation, 15(5, 6):463-465, 1993.
@article{bibel/biermann:1993,
  author = {W. Bibel and A. W. Biermann},
  title = {Special Issue: Automatic Programming, foreword of the
		  guest editors},
  journal = {Journal of Symbolic Computation},
  year = 1993,
  volume = 15,
  number = {5, 6},
  pages = {463--465}
}
 
W. Bibel. Syntax-directed, semantic-supported program synthesis. Artificial Intelligence, 14(3):243-261, 1980.
@article{bibel:1980,
  author = {W. Bibel},
  title = {Syntax-directed, semantic-supported program synthesis},
  journal = {Artificial Intelligence},
  year = 1980,
  volume = 14,
  number = 3,
  pages = {243--261}
}
 
W. Bibel, D. Korn, C. Kreitz, and F. Kurucz. A multi-level approach to program synthesis. In Norbert E. Fuchs, editor, Logic Programming Synthesis and Transformation. 7th International Workshop, LOPSTR'97, Leuven, Belgium, July10-12, 1997. Proceedings, volume 1207 of Lecture Notes in Computer Science, pages 1-27, Berlin/Heidelberg, 1998. Springer.
@inproceedings{bibel_ea:1998,
  author = {W. Bibel and D. Korn and C. Kreitz and F. Kurucz},
  title = {A Multi-level Approach to Program Synthesis},
  editor = {Norbert E. Fuchs},
  booktitle = {Logic Programming Synthesis and Transformation. 7th
		  International Workshop, {LOPSTR'97}, Leuven, Belgium,
		  July\,10--12, 1997. Proceedings},
  year = 1998,
  series = {Lecture Notes in Computer Science},
  volume = 1207,
  pages = {1--27},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  doi = {10.1007/3-540-49674-2_1},
  url = {http://www.springerlink.com/content/d4ud8wf2gkpcll2q/},
  abstract = {We present an approach to a coherent program synthesis
		  system which integrates a variety of interactively
		  controlled and automated techniques from theorem proving
		  and algorithm design at different levels of abstraction.
		  Besides providing an overall view we summarize the
		  individual research results achieved in the course of this
		  development.},
  isbn = {978-3-540-65074-4}
}
 
Alan W. Biermann and Gerard Guiho, editors. Computer Program Synthesis Methodologies. Reidel, 1983.
@book{biermann/guiho:1983,
  editor = {Alan W. Biermann and Gerard Guiho},
  title = {Computer Program Synthesis Methodologies},
  publisher = {Reidel},
  year = 1983
}
 
Alan W. Biermann and R. Krishnaswamy. Constructing programs from example computations. IEEE Transactions on Software Engineering, 2(3):141-153, 1976.
@article{biermann/krishnaswamy:1976,
  author = {Alan W. Biermann and R. Krishnaswamy},
  title = {Constructing Programs from Example Computations},
  journal = {IEEE Transactions on Software Engineering},
  year = 1976,
  volume = 2,
  number = 3,
  pages = {141--153},
  address = {Los Alamitos, CA, USA},
  publisher = {IEEE Computer Society},
  keywords = {ase; induction; inductive programming; pre-summers;
		  program synthesis; synthesis from traces},
  url = {http://doi.ieeecomputersociety.org/10.1109/TSE.1976.233812},
  abstract = {An autoprogrammer is an interactive computer programming
		  system which automatically constructs computer programs
		  from example computations executed by the user. The example
		  calculations are done in a scratch pad fashion at a
		  computer display using a light pen or other graphic input
		  device, and the system stores a detailed history of all of
		  the steps executed in the process. Then the system
		  automatically synthesizes the shortest possible program
		  which is capable of executing the observed examples. The
		  paper describes the computational environment provided by
		  the system, proves that the program synthesis technique is
		  both "sound" and "complete," describes the design of the
		  system, and gives some programs it was used to create.}
}
 
Alan W. Biermann and Douglas R. Smith. The hierarchical synthesis of LISP scanning programs. In B. Gilchrist, editor, Information Processing 77, pages 41-45, Amsterdam, 1977. North-Holland Publishing.
@inproceedings{biermann/smith:1977,
  author = {Biermann, Alan W. and Smith, Douglas R.},
  title = {The Hierarchical Synthesis of {LISP} Scanning Programs},
  editor = {B. Gilchrist},
  booktitle = {Information Processing 77},
  year = 1977,
  pages = {41--45},
  address = {Amsterdam},
  publisher = {North-Holland Publishing},
  keywords = {analytical ip; ifp; induction; inductive programming;
		  program synthesis},
  annote = {Surveyed, amongst others, in Smith, The Synthesis of LISP
		  programs from Examples: A Survey, 1984.}
}
 
Alan W. Biermann and Douglas R. Smith. A production rule mechanism for generating LISP code. IEEE Transactions on Systems, Man, and Cybernetics, 9(5):260-276, 1979.
@article{biermann/smith:1979,
  author = {Biermann, Alan W. and Smith, Douglas R.},
  title = {A Production Rule Mechanism for Generating {LISP} Code},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics},
  year = 1979,
  volume = 9,
  number = 5,
  pages = {260--276},
  url = {http://ieeexplore.ieee.org/Xplore/login.jsp?url=/iel5/21/4310191/04310195.pdf?arnumber=4310195},
  keywords = {analytical ip; ifp; induction; inductive programming;
		  program synthesis},
  annote = {Surveyed in Smith, The Synthesis of LISP programs from
		  Examples: A Survey, 1984},
  abstract = {Production rule schemas are given which hold the basic
		  information necessary for coding recursive loops and
		  branches in LISP. Information from the user concerning the
		  desired program is used to instantiate the schemas to yield
		  production rules, and then these rules generate executable
		  code in a strictly syntactic fashion. Emphasis is placed on
		  decomposing the synthesis problem into a hierarchy of tasks
		  which can each be solved by application of a schema. The
		  method is demonstrated by showing how programs can be
		  synthesized from examples of their input-output
		  behaviors.}
}
 
Alan W. Biermann. On the inference of Turing machines from sample computations. Artificial Intelligence, 3(3):181-198, 1972.
@article{biermann:1972,
  author = {Biermann, Alan W.},
  title = {On the inference of {Turing} machines from sample
		  computations},
  journal = {Artificial Intelligence},
  year = 1972,
  volume = 3,
  number = 3,
  pages = {181--198}
}
 
Alan W. Biermann. The inference of regular LISP programs from examples. IEEE Transactions on Systems, Man and Cybernetics, 8(8):585-600, 1978.
@article{biermann:1978,
  author = {Biermann, Alan W.},
  title = {The Inference of Regular {LISP} Programs from Examples},
  journal = {IEEE Transactions on Systems, Man and Cybernetics},
  year = 1978,
  volume = 8,
  number = 8,
  pages = {585--600},
  url = {http://ieeexplore.ieee.org/Xplore/login.jsp?url=/iel5/21/4310032/04310035.pdf?arnumber=4310035},
  keywords = {analytical ip; ifp; induction; inductive programming;
		  program synthesis; synthesis from traces},
  annote = {Surveyed in Smith, The Synthesis of LISP Programs from
		  Examples: A Survey, 1984},
  abstract = {A class of LISP programs that is analogous to the
		  finite-state automata is defined, and an algorithm is given
		  for constructing such programs from examples of their
		  input-output behavior. It is shown that the algorithm has
		  robust performance for a wide variety of inputs and that it
		  converges to a solution on the basis of minimum input
		  information. }
}
 
Alan W. Biermann. Dealing with search. In Alan W. Biermann, Yves Kodratoff, and Gerard Guiho, editors, Automatic Program Construction Techniques, chapter 17, pages 375-392. The Free Press, New York, NY, USA, 1984.
@incollection{biermann:1984,
  author = {Biermann, Alan W.},
  title = {Dealing With Search},
  editor = {Alan W. Biermann and Yves Kodratoff and Gerard Guiho},
  booktitle = {Automatic Program Construction Techniques},
  publisher = {The Free Press},
  year = 1984,
  chapter = 17,
  pages = {375--392},
  address = {New York, NY, USA},
  isbn = 0029490707,
  keywords = {analytical ip; enumerative ip; ifp; induction; inductive
		  programming; lisp; program synthesis},
  annote = {overview over the synthesis of regular and scanning LISP
		  programs}
}
 
Alan W. Biermann. Automatic programming: a tutorial on formal methodologies. Journal of Symbolic Compututation, 1(2):119-142, 1985.
@article{biermann:1985,
  author = {Biermann, Alan W.},
  title = {Automatic Programming: a Tutorial on Formal
		  Methodologies},
  journal = {Journal of Symbolic Compututation},
  year = 1985,
  volume = 1,
  number = 2,
  pages = {119--142},
  address = {Duluth, MN, USA},
  publisher = {Academic Press},
  keywords = {analytical ip; ase; deductive program synthesis;
		  enumerative ip; ifp; induction; inductive programming;
		  lisp; program synthesis},
  annote = {overview of deductive, inductive, from natural language
		  automatic programming methods},
  url = {http://dx.doi.org/10.1016/S0747-7171(85)80010-9}
}
 
Alan W. Biermann. Automatic programming. In Stuart C. Shapiro, editor, Encyclopedia of Artificial Intelligence, pages 18-35. John Wiley & Sons, Inc., New York, NY, USA, 2 edition, 1992.
@incollection{biermann:1992,
  author = {Biermann, Alan W.},
  title = {Automatic Programming},
  editor = {Stuart C. Shapiro},
  booktitle = {Encyclopedia of Artificial Intelligence},
  publisher = {John Wiley \& Sons, Inc.},
  year = 1992,
  pages = {18--35},
  address = {New York, NY, USA},
  edition = 2,
  keywords = {analytical ip; ase; deductive program synthesis;
		  enumerative ip; formal methods; ifp; ilp; induction;
		  inductive programming; lisp; overview; program synthesis},
  annote = {overview of deductive, inductive, ilp, and from natural
		  language automatic programming methods}
}
 
Alan W. Biermann, R. I. Baum, and F. E. Petry. Speeding up the synthesis of programs from traces. IEEE Transactions on Computers, 24(2):122-136, 1975.
@article{biermann_ea:1975,
  author = {Alan W. Biermann and R. I. Baum and F. E. Petry},
  title = {Speeding up the Synthesis of Programs from Traces},
  journal = {IEEE Transactions on Computers},
  year = 1975,
  volume = 24,
  number = 2,
  pages = {122--136},
  address = {Los Alamitos, CA, USA},
  publisher = {IEEE Computer Society},
  url = {http://doi.ieeecomputersociety.org/10.1109/T-C.1975.224180},
  keywords = {analytical ip; enumerative ip; induction; inductive
		  programming; program synthesis; synthesis from traces},
  abstract = {An algorithm is given for synthesizing a computer program
		  from a trace of its behavior. Since the algorithm involves
		  a search, the length of time required to do the synthesis
		  of nontrivial programs can be quite large. Techniques are
		  given for preprocessing the trace information to reduce
		  enumeration, for pruning the search using a failure memory
		  technique, and for utilizing multiple traces to the best
		  advantage. The results of numerous tests are given to
		  demonstrate the value of the techniques.}
}
 
Alan W. Biermann, Yves Kodratoff, and Gerard Guiho. Automatic Program Construction Techniques. The Free Press, New York, NY, USA, 1984.
@book{biermann_ea:1984,
  author = {Alan W. Biermann and Yves Kodratoff and Gerard Guiho},
  title = {Automatic Program Construction Techniques},
  publisher = {The Free Press},
  year = 1984,
  address = {New York, NY, USA},
  isbn = 0029490707
}
 
Franck Binard and Amy Felty. An abstraction-based genetic programming system. In Dirk Thierens, editor, GECCO'07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (London, England, UK, July7-11, 2007). Companion Material, pages 2415-2422, New York, NY, USA, 2007. ACM. Session “Late-breaking papers”.
@inproceedings{binard/felty:2007,
  author = {Binard, Franck and Felty, Amy},
  title = {An abstraction-based genetic programming system},
  editor = {Dirk Thierens},
  booktitle = {{GECCO'07}: Proceedings of the 9th Annual Conference on
		  Genetic and Evolutionary Computation (London, England, UK,
		  July\,7--11, 2007). Companion Material},
  year = 2007,
  pages = {2415--2422},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  note = {Session ``Late-breaking papers''},
  url = {http://doi.acm.org/10.1145/1274000.1274004},
  isbn = {978-1-59593-698-1}
}
 
Franck Binard and Amy Felty. Genetic programming with polymorphic types and higher-order functions. In Conor Ryan and Maarten Keijzer, editors, GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (Atlanta, GA, USA, July12-16, 2008), pages 1187-1194, New York, NY, USA, 2008. ACM. Session “Genetic programming papers”.
@inproceedings{binard/felty:2008,
  author = {Franck Binard and Amy Felty},
  title = {Genetic Programming with Polymorphic Types and
		  Higher-Order Functions},
  editor = {Conor Ryan and Maarten Keijzer},
  booktitle = {{GECCO'08}: Proceedings of the 10th Annual Conference on
		  Genetic and Evolutionary Computation (Atlanta, GA, USA,
		  July\,12--16, 2008)},
  year = 2008,
  pages = {1187--1194},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  note = {Session ``Genetic programming papers''},
  isbn = {978-1-60558-130-9},
  url = {http://doi.acm.org/10.1145/1389095.1389330},
  keywords = {enumerative ip; gp; higher-order functions; ifp;
		  induction; inductive programming; program evolution;
		  program synthesis}
}
 
Holger Bischof, Sergei Gorlatch, and Emanuel Kitzelmann. The double-scan skeleton and its parallelization. Technical report, Technische Universität Berlin, 2002.
@techreport{bischof_ea:2002,
  author = {Holger Bischof and Sergei Gorlatch and Emanuel
		  Kitzelmann},
  title = {The Double-Scan Skeleton and its Parallelization},
  institution = {{Technische Universit{\"a}t Berlin}},
  year = 2002,
  keywords = {parallel programming}
}
 
Holger Bischof, Sergei Gorlatch, and Emanuel Kitzelmann. Cost optimality and predictability of parallel programming with skeletons. Parallel Processing Letters, 13(4):575-587, 2003.
@article{bischof_ea:2003,
  author = {Holger Bischof and Sergei Gorlatch and Emanuel
		  Kitzelmann},
  title = {Cost Optimality And Predictability Of Parallel Programming
		  with Skeletons},
  journal = {Parallel Processing Letters},
  year = 2003,
  volume = 13,
  number = 4,
  pages = {575--587},
  url = {http://dx.doi.org/10.1142/S0129626403001525},
  keywords = {article; parallel programming; skeletons}
}
 
Holger Bischof, Sergei Gorlatch, and Emanuel Kitzelmann. Design and implementation of a cost-optimal parallel tridiagonal system solver using skeletons. In Parallel Computing Technologies, volume 2763 of Lecture Notes in Computer Science, pages 415-428, Berlin/Heidelberg, 2003. Springer.
@inproceedings{bischof_ea:2003b,
  author = {Holger Bischof and Sergei Gorlatch and Emanuel
		  Kitzelmann},
  title = {Design and Implementation of a Cost-Optimal Parallel
		  Tridiagonal System Solver Using Skeletons},
  booktitle = {Parallel Computing Technologies},
  year = 2003,
  series = {Lecture Notes in Computer Science},
  volume = 2763,
  pages = {415--428},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-40673-0},
  url = {http://www.springerlink.com/content/j1362563434l61n6},
  doi = {10.1007/978-3-540-45145-7_39},
  keywords = {inproceedings; parallel programming; skeletons;
		  tridiagonal system solver},
  abstract = {We address the problem of systematically designing correct
		  parallel programs and developing their efficient
		  implementations on parallel machines. The design process
		  starts with an intuitive, sequential algorithm and proceeds
		  by expressing it in terms of well-defined, pre-implemented
		  parallel components called skeletons. We demonstrate the
		  skeleton-based design process using the tridiagonal system
		  solver as our example application. We develop step by step
		  three provably correct, parallel versions of our
		  application, and finally arrive at a cost-optimal
		  implementation in MPI (Message Passing Interface). The
		  performance of our solutions is demonstrated experimentally
		  on a Cray T3E machine.}
}
 
Holger Bischof, Sergei Gorlatch, and Emanuel Kitzelmann. Cost optimality and predictability of parallel programming with skeletons. In Euro-Par 2003 Parallel Processing. 9th International Euro-Par Conference, Klagenfurt, Austria, Aug.26-29, 2003. Proceedings, volume 2790 of Lecture Notes in Computer Science, pages 682-693, Berlin/Heidelberg, 2004. Springer.
@inproceedings{bischof_ea:2004,
  author = {Holger Bischof and Sergei Gorlatch and Emanuel
		  Kitzelmann},
  title = {Cost Optimality and Predictability of Parallel Programming
		  with Skeletons},
  booktitle = {{Euro-Par 2003 Parallel Processing}. 9th International
		  Euro-Par Conference, Klagenfurt, Austria, Aug.\,26--29,
		  2003. Proceedings},
  year = 2004,
  series = {Lecture Notes in Computer Science},
  volume = 2790,
  pages = {682--693},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  keywords = {inproceedings; parallel programming; skeletons},
  abstract = {Skeletons are reusable, parameterized components with
		  well-defined semantics and pre-packaged efficient parallel
		  implementation. This paper develops a new, provably
		  cost-optimal implementation of the DS (double-scan)
		  skeleton for the divide-and-conquer paradigm. Our
		  implementation is based on a novel data structure called
		  plist (pointed list); implementation's performance is
		  estimated using an analytical model. We demonstrate the use
		  of the DS skeleton for parallelizing a tridiagonal system
		  solver and report experimental results for its MPI
		  implementation on a Cray T3E and a Linux cluster: they
		  confirm the performance improvement achieved by the
		  cost-optimal implementation and demonstrate its good
		  predictability by our performance model.},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-40788-1},
  url = {http://www.springerlink.com/content/602ycb05htd85f24},
  doi = {10.1007/978-3-540-45209-6_97}
}
 
Henrik Boström. Specialization of recursive predicates. In Nada Lavrac and Stefan Wrobel, editors, Machine Learning: ECML-95. 8th European Conference on Machine Learning Heraclion, Crete, Greece, April25-27, 1995. Proceedings, volume 912 of Lecture Notes in Computer Science, pages 92-106, Berlin/Heidelberg, 1995. Springer.
@inproceedings{bostroem:1995,
  author = {Henrik Bostr\"{o}m},
  title = {Specialization of Recursive Predicates},
  editor = {Nada Lavrac and Stefan Wrobel},
  booktitle = {Machine Learning: {ECML-95}. 8th European Conference on
		  Machine Learning Heraclion, Crete, Greece, April\,25--27,
		  1995. Proceedings},
  year = 1995,
  series = {Lecture Notes in Computer Science},
  volume = 912,
  pages = {92--106},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-59286-0},
  url = {http://www.springerlink.com/content/f463100m0744736q/},
  doi = {10.1007/3-540-59286-5_51},
  keywords = {SPECTRE; ilp; inductive programming; ip-system; program
		  synthesis; recursion},
  abstract = {When specializing a recursive predicate in order to
		  exclude a set of negative examples without excluding a set
		  of positive examples, it may not be possible to specialize
		  or remove any of the clauses in a refutation of a negative
		  example without excluding any positive examples. A
		  previously proposed solution to this problem is to apply
		  program transformation in order to obtain non-recursive
		  target predicates from recursive ones. However, the
		  application of this method prevents recursive
		  specializations from being found. In this work, we present
		  the algorithm SPECTRE II which is not limited to
		  specializing non-recursive predicates. The key idea upon
		  which the algorithm is based is that it is not enough to
		  specialize or remove clauses in refutations of negative
		  examples in order to obtain correct specializations, but it
		  is sometimes necessary to specialize clauses that appear
		  only in refutations of positive examples. In contrast to
		  its predecessor SPECTRE, the new algorithm is not limited
		  to specializing clauses defining one predicate only, but
		  may specialize clauses defining multiple predicates.
		  Furthermore, the positive and negative examples are no
		  longer required to be instances of the same predicate. It
		  is proven that the algorithm produces a correct
		  specialization when all positive examples are logical
		  consequences of the original program, there is a finite
		  number of derivations of positive and negative examples and
		  when no positive and negative examples have the same
		  sequence of input clauses in their refutations.}
}
 
H. Boström. Theory-guided induction of logic programs by inference of regular languages. In Lorenza Saitta, editor, ICML '96: Proceedings of the Thirteenth International Conference on Machine Learning (Bari, Italy, July3-6, 1996), pages 46-53. Morgan Kaufmann, 1996.
@inproceedings{bostroem:1996,
  author = {H. Bostr{\"o}m},
  title = {Theory-guided induction of logic programs by inference of
		  regular languages},
  editor = {Lorenza Saitta},
  booktitle = {{ICML '96}: Proceedings of the Thirteenth International
		  Conference on Machine Learning (Bari, Italy, July\,3--6,
		  1996)},
  year = 1996,
  pages = {46--53},
  publisher = {Morgan Kaufmann},
  isbn = {1-55860-419-7}
}
 
H. Boström. Predicate invention and learning from positive examples only. In Claire Nedellec and Céline Rouveirol, editors, Machine Learning: ECML-98. 10th European Conference on Machine Learning, Chemnitz, Germany, April21-23, 1998. Proceedings, volume 1398 of Lecture Notes in Computer Science, pages 226-237, Berlin/Heidelberg, 1998. Springer.
@inproceedings{bostroem:1998,
  author = {H. Bostr{\"o}m},
  title = {Predicate Invention and Learning from Positive Examples
		  Only},
  editor = {Claire Nedellec and C{\'e}line Rouveirol},
  booktitle = {Machine Learning: {ECML-98}. 10th European Conference on
		  Machine Learning, Chemnitz, Germany, April\,21--23, 1998.
		  Proceedings},
  year = 1998,
  series = {Lecture Notes in Computer Science},
  volume = 1398,
  pages = {226--237},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-64417-0},
  url = {http://www.springerlink.com/content/5h0440715vx65g71/},
  abstract = {Previous bias shift approaches to predicate invention are
		  not applicable to learning from positive examples only, if
		  a complete hypothesis can be found in the given language,
		  as negative examples are required to determine whether new
		  predicates should be invented or not. One approach to this
		  problem is presented, MERLIN 2.0, which is a successor of a
		  system in which predicate invention is guided by sequences
		  of input clauses in SLD-refutations of positive and
		  negative examples w.r.t. an overly general theory. In
		  contrast to its predecessor which searches for the minimal
		  finite-state automaton that can generate all positive and
		  no negative sequences, MERLIN 2.0 uses a technique for
		  inducing Hidden Markov Models from positive sequences only.
		  This enables the system to invent new predicates without
		  being triggered by negative examples. Another advantage of
		  using this induction technique is that it allows for
		  incremental learning. Experimental results are presented
		  comparing MERLIN 2.0 with the positive only learning
		  framework of Progol 4.2 and comparing the original
		  induction technique with a new version that produces
		  deterministic Hidden Markov Models. The results show that
		  predicate invention may indeed be both necessary and
		  possible when learning from positive examples only as well
		  as it can be beneficial to keep the induced model
		  deterministic.},
  doi = {10.1007/BFb0026693}
}
 
H. Boström. Induction of recursive transfer rules. In Cussens J., editor, Learning Language in Logic, volume 1925 of Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, pages 369-450. Springer, Berlin/Heidelberg, 2000.
@incollection{bostroem:2000,
  author = {H. Bostr{\"o}m},
  title = {Induction of Recursive Transfer Rules},
  editor = {Cussens J.},
  booktitle = {{Learning Language in Logic}},
  publisher = {Springer},
  year = 2000,
  volume = 1925,
  series = {Lecture Notes in Computer Science. Lecture Notes in
		  Artificial Intelligence},
  pages = {369--450},
  address = {Berlin\,/\,Heidelberg},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-41145-1},
  url = {http://www.springerlink.com/content/01pxw099u4u4cwxp/},
  abstract = {Transfer rules are used in bi-lingual translation systems
		  for transferring a logical representation of a source
		  language sentence into a logical representation of the
		  corresponding target language sentence. This work studies
		  induction of transfer rules from examples of corresponding
		  pairs of source-target quasi logical formulae (QLFs). The
		  main features of this problem are: i) more than one rule
		  may need to be produced from a single example, ii) only
		  positive examples are provided and iii) the produced
		  hypothesis should be recursive. In an earlier study of this
		  problem, a system was proposed in which hand-coded
		  heuristics were employed for identifying non-recursive
		  correspondences. In this work we study the case when
		  non-recursive transfer rules have been given to the system
		  instead of heuristics. Results from a preliminary
		  experiment with English-French QLFs are presented,
		  demonstrating that this information is sufficient for the
		  generation of generally applicable rules that can be used
		  for transfer between previously unseen source and target
		  QLFs. However, the experiment also shows that the system
		  suffers from producing overly specific rules, even when the
		  problem of disallowing the derivation of other target QLFs
		  than the correct one is not considered. Potential
		  approaches to this problem are discussed.},
  doi = {10.1007/3-540-40030-3_15}
}
 
A. F. Bowers, C. Giraud-Carrier, C. Kennedy, J. W. Lloyd, and R. MacKinney-Romero. A framework for higher-order inductive machine learning. In Peter A. Flach and Nada Lavrac, editors, Proceedings of the CompulogNet Area Meeting on Representation issues in reasoning and learning (CSTR-97-005, Department of Computer Science, University of Bristol. Sept.20, 1997), 1997. In conjunction with the Seventh International Workshop on Inductive Logic Programming ILP'97.
@inproceedings{bowers_ea:1997,
  author = {A. F. Bowers and C. Giraud-Carrier and C. Kennedy and J.
		  W. Lloyd and R. MacKinney-Romero},
  title = {A Framework for Higher-Order Inductive Machine Learning},
  editor = {Peter A. Flach and Nada Lavrac},
  booktitle = {Proceedings of the {CompulogNet} Area Meeting on
		  Representation issues in reasoning and learning
		  ({CSTR-97-005}, Department of Computer Science, University
		  of Bristol. Sept.\,20, 1997)},
  year = 1997,
  note = {In conjunction with the Seventh International Workshop on
		  Inductive Logic Programming {ILP'97}}
}
 
Robert S. Boyer and J. Strother Moore. Proving theorems about LISP functions. Journal of the ACM, 22(1):129-144, January 1975.
@article{boyer/moore:1975,
  author = {Boyer, Robert S. and Moore, J. Strother},
  title = {Proving Theorems about {LISP} Functions},
  journal = {Journal of the {ACM}},
  year = 1975,
  volume = 22,
  number = 1,
  pages = {129--144},
  month = {January},
  address = {New York, NY, USA},
  annote = {The BMWk algo of Kodratoff et al is named based on this
		  paper.},
  publisher = {{ACM}},
  keywords = {lisp; theorem proving},
  url = {http://doi.acm.org/10.1145/321864.321875},
  abstract = {Program verification is the idea that properties of
		  programs can be precisely stated and proved in the
		  mathematical sense. In this paper, some simple heuristics
		  combining evaluation and mathematical induction are
		  described, which the authors have implemented in a program
		  that automatically proves a wide variety of theorems about
		  recursive LISP functions. The method the program uses to
		  generate induction formulas is described at length. The
		  theorems proved by the program include that REVERSE is its
		  own inverse and that a particular SORT program is correct.
		  A list of theorems proved by the program is given.}
}
 
Ivan Bratko and Stephen H. Muggleton. Applications of inductive logic programming. Communications of the ACM, 38(11):65-70, 1995.
@article{bratko/muggleton:1995,
  author = {Ivan Bratko and Stephen H. Muggleton},
  title = {Applications of Inductive Logic Programming},
  journal = {Communications of the {ACM}},
  year = 1995,
  volume = 38,
  number = 11,
  pages = {65--70},
  keywords = {1995; applications; article; ilp; induction; inductive
		  inference; survey},
  doi = {http://doi.acm.org/10.1145/219717.219771},
  abstract = {Techniques of machine learning have been successfully
		  applied to various problems. Most of these applications
		  rely on attribute-based learning, exemplified by the
		  induction of decision trees as in the program C4.5. Broadly
		  speaking, attribute-based learning also includes such
		  approaches to learning as neural networks and nearest
		  neighbor techniques. The advantages of attribute-based
		  learning are: relative simplicity, efficiency, and
		  existence of effective techniques for handling noisy data.
		  However, attribute-based learning is limited to
		  non-relational descriptions of objects in the sense that
		  the learned descriptions do not specify relations among the
		  objects' parts. Attribute-based learning thus has two
		  strong limitations: the background knowledge can be
		  expressed in rather limited form, and the lack of relations
		  makes the concept description language inappropriate for
		  some domains.}
}
 
Ivan Bratko. Prolog Programming for Artificial Intelligence. Addison-Wesley, 1986.
@book{bratko:1986,
  author = {Ivan Bratko},
  title = {Prolog Programming for Artificial Intelligence},
  publisher = {Addison-Wesley},
  year = 1986,
  keywords = {prolog}
}
 
A Brazma and E. B. Kinber. Generalized regular expressions - a language for synthesis of programs with branching in loops. Theoretical Computer Science, 46:175-195, 1986.
@article{brazma/kinber:1986,
  author = {Br{\=a}zma, A and Kinber, E. B.},
  title = {Generalized regular expressions -- a language for
		  synthesis of programs with branching in loops},
  journal = {Theoretical Computer Science},
  year = 1986,
  volume = 46,
  pages = {175--195},
  publisher = {Elsevier Science Publishers Ltd.},
  address = {Essex, UK}
}
 
A. Brazma. Inductive synthesis of dot expressions. In J. M. Barzdinš and D. Bjorner, editors, Baltic Computer Science. Selected Papers, volume 502 of Lecture Notes in Computer Science, pages 156-212. Springer, Berlin/Heidelberg, 1991.
@incollection{brazma:1991,
  author = {A. Br{\=a}zma},
  title = {Inductive synthesis of dot expressions},
  editor = {B{\=a}rzdi{\c{n}}{\v{s}}, J. M. and Bjorner, D.},
  booktitle = {Baltic Computer Science. Selected Papers},
  publisher = {Springer},
  year = 1991,
  volume = 502,
  series = {Lecture Notes in Computer Science},
  pages = {156--212},
  address = {Berlin\,/\,Heidelberg},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-54131-8},
  url = {http://www.springerlink.com/content/57332j2671j7p508/},
  abstract = {We consider the problem of the synthesis of algorithms by
		  sample computations. We introduce a formal language,
		  namely, the so-called dot expressions, which is based on a
		  formalization of the intuitive notion of ellipsis (......).
		  Whilst formally the dot expressions are simply a language
		  describing sets of words, on the other hand, it can be
		  considered as a programming language supporting quite a
		  wide class of programs. Equivalence and asymptotical
		  equivalence of dot expressions are defined and proved to be
		  decidable. A formal example of a dot expression is defined
		  in the way that, actually, it represents a sample
		  computation of the program presented by the given dot
		  expression. A system of simple inductive inference rules
		  synthesizing dot expressions (programs) by their formal
		  examples (sample computations) is developed and proved to
		  synthesize a correct (i.e., asymptotically equivalent to
		  the given) expression by one sufficiently long example.
		  Some instances of the application of the model for program
		  inductive synthesis are also given. Particularly, there are
		  given examples of the euclidean and bubblesort algorithm
		  synthesis within acceptable time from completely natural
		  sample descriptions.). Whilst formally the dot expressions
		  are simply a language describing sets of words, on the
		  other hand, it can be considered as a programming language
		  supporting quite a wide class of programs. Equivalence and
		  asymptotical equivalence of dot expressions are defined and
		  proved to be decidable. A formal example of a dot
		  expression is defined in the way that, actually, it
		  represents a sample computation of the program presented by
		  the given dot expression. A system of simple inductive
		  inference rules synthesizing dot expressions (programs) by
		  their formal examples (sample computations) is developed
		  and proved to synthesize a correct (i.e., asymptotically
		  equivalent to the given) expression by one sufficiently
		  long example. Some instances of the application of the
		  model for program inductive synthesis are also given.
		  Particularly, there are given examples of the euclidean and
		  bubblesort algorithm synthesis within acceptable time from
		  completely natural sample descriptions.},
  doi = {10.1007/BFb0019359}
}
 
R. M. Burstall and John Darlington. A transformation system for developing recursive programs. Journal of the ACM, 24(1):44-67, January 1977.
@article{burstall/darlington:1977,
  author = {R. M. Burstall and John Darlington},
  title = {A Transformation System for Developing Recursive
		  Programs},
  journal = {Journal of the {ACM}},
  year = 1977,
  volume = 24,
  number = 1,
  pages = {44--67},
  month = {January},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  url = {http://doi.acm.org/10.1145/321992.321996},
  keywords = {article; ase; deductive program synthesis; program
		  optimisation; program synthesis; program transformation},
  annote = {A Transformation System for Developing Recursive
		  Programs},
  abstract = {A system of rules for transforming programs is described,
		  with the programs in the form of recursion equations. An
		  initially very simple, lucid, and hopefully correct program
		  is transformed into a more efficient one by altering the
		  recursion structure. Illustrative examples of program
		  transformations are given, and a tentative implementation
		  is described. Alternative structures for programs are
		  shown, and a possible initial phase for an automatic or
		  semiautomatic program-manipulation system is indicated.}
}
 
C. T. P. Burton. Program morphisms. Formal Aspects of Computing, 4:693-726, 1992.
@article{burton:1992,
  author = {C. T. P. Burton},
  title = {Program Morphisms},
  journal = {Formal Aspects of Computing},
  year = 1992,
  volume = 4,
  pages = {693--726},
  keywords = {category; definition; functional; parallel; recursive;
		  theory; transformation},
  abstract = {An algebraic view of recursive definitions is presented,
		  extending an already familiar analogy with homomorphisms. A
		  notion of simulation of one recursive definition by another
		  is then defined. This leads to a particular approach to
		  verification and transformation, which places emphasis on
		  the arrows between programs, rather than the programs
		  themselves. These arrows are the program morphisms of the
		  title. Examples are given, together with certain extensions
		  of the idea. Also indicated is a methodology which can lead
		  to the discovery of program morphisms and new equivalent
		  versions of a given program.},
  annote = {ute-inflit}
}
 
Rui Camacho, Ross D. King, and Ashwin Srinivasan, editors. Inductive Logic Programming, 14th International Conference, ILP'04, Porto, Portugal, Sept.6-8, 2004, Proceedings, volume 3194 of Lecture Notes in Computer Science, Berlin/Heidelberg, 2004. Springer.
@proceedings{camacho_ea:2004,
  title = {Inductive Logic Programming, 14th International
		  Conference, {ILP'04}, Porto, Portugal, Sept.\,6--8, 2004,
		  Proceedings},
  year = 2004,
  editor = {Rui Camacho and Ross D. King and Ashwin Srinivasan},
  volume = 3194,
  series = {Lecture Notes in Computer Science},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-22941-4},
  url = {http://www.springerlink.com/content/j9u24yq8m52r/},
  doi = {10.1007/b10011}
}
 
R. Mike Cameron-Jones and J. Ross Quinlan. Avoiding pitfalls when learning recursive theories. In Ruzena Bajcsy, editor, IJCAI'93: Proceedings of the 13th International Joint Conference on Artificial Intelligence (Chambéry, France, Aug.28-Sep.3, 1993), pages 1050-1057. Morgan Kaufmann, 1993.
@inproceedings{cameron-jones/quinlan:1993,
  author = {R. Mike Cameron-Jones and J. Ross Quinlan},
  title = {Avoiding Pitfalls When Learning Recursive Theories},
  editor = {Ruzena Bajcsy},
  booktitle = {{IJCAI}'93: Proceedings of the 13th International Joint
		  Conference on Artificial Intelligence (Chamb\'ery, France,
		  Aug.\,28--Sep.\,3, 1993)},
  year = 1993,
  pages = {1050--1057},
  publisher = {Morgan Kaufmann},
  annote = {foil, assuring termination for recursive theories},
  documenturl = {http://www.rulequest.com/Personal/cj+q.ijcai93.ps},
  keywords = {enumerative ip; foil; ilp; induction; inductive
		  programming; inproceedings; machine learning; program
		  synthesis}
}
 
R. Mike Cameron-Jones and J. Ross Quinlan. Efficient top-down induction of logic programs. SIGART Bulletin, 5(1):33-42, January 1994.
@article{cameron-jones/quinlan:1994,
  author = {R. Mike Cameron-Jones and J. Ross Quinlan},
  title = {Efficient Top-Down Induction of Logic Programs},
  journal = {SIGART Bulletin},
  year = 1994,
  volume = 5,
  number = 1,
  pages = {33--42},
  month = {January},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  url = {http://doi.acm.org/10.1145/181668.181676},
  keywords = {applications; article; enumerative ip; foil; ilp;
		  induction; inductive programming; machine learning; program
		  synthesis},
  annote = {foil in 1994},
  abstract = {FOIL is a system for inducing function-free Horn clause
		  definitions of relations from example and extensionally
		  defined background relations. It demonstrates the
		  successful application of a general to specific approach to
		  clause induction using heuristically guided search. This
		  paper describes the current version of FOIL, assesses its
		  performance and notes areas for improvement. The successful
		  application of similar methods in other systems is reviewed
		  to demonstrate their general utility.}
}
 
Baudouin Le Charlier and Pierre Flener. Specifications are necessarily informal or: Some more myths of formal methods. Journal of Systems and Software, 40(3):275-296, 1998.
@article{charlier/flener:1998,
  author = {Baudouin Le Charlier and Pierre Flener},
  title = {Specifications are necessarily informal or: Some more
		  myths of formal methods},
  journal = {Journal of Systems and Software},
  year = 1998,
  volume = 40,
  number = 3,
  pages = {275--296},
  address = {New York, NY, USA},
  publisher = {Elsevier Science Inc.},
  url = {http://dx.doi.org/10.1016/S0164-1212(98)00172-1},
  keywords = {article; ase; comparison; formal methods; position paper;
		  program synthesis; software engineering}
}
 
H. D. Cheng and K. S. Fu. Algorithm partition and parallel recognition of general context-free languages using fixed-size VLSI architecture. Pattern Recognition, 19(5):361-372, 1986.
@article{cheng/fu:1986,
  author = {H. D. Cheng and K. S. Fu},
  title = {Algorithm Partition and Parallel Recognition of General
		  Context-Free Languages Using Fixed-Size {VLSI}
		  Architecture},
  journal = {Pattern Recognition},
  year = 1986,
  volume = 19,
  number = 5,
  pages = {361--372},
  publisher = {Elsevier Science Inc.},
  address = {New York, NY, USA},
  keywords = {language; matematics}
}
 
V. Ciesielski and Xiang Li. Experiments with explicit for-loops in genetic programming. In CEC'04: Proceedings of the IEEE Congress on Evolutionary Computation (Portland, Oregon June20-23, 2004), pages 494-501. IEEE Press, 2004.
@inproceedings{ciesielski/li:2004,
  author = {V. Ciesielski and Xiang Li},
  title = {Experiments with Explicit For-Loops in Genetic
		  Programming},
  booktitle = {{CEC'04}: Proceedings of the IEEE Congress on Evolutionary
		  Computation (Portland, Oregon June\,20--23, 2004)},
  year = 2004,
  pages = {494--501},
  publisher = {IEEE Press},
  url = {http://dx.doi.org/10.1109/CEC.2004.1330897},
  keywords = {enumerative ip; experiment; gp; induction; inductive
		  programming; loops; program evolution; program synthesis},
  abstract = {Evolving programs with explicit loops presents major
		  difficulties, primarily due to the massive increase in the
		  size of the search space. Fitness evaluation becomes
		  computationally expensive and a method for dealing with
		  infinite loops must be implemented. We have investigated
		  ways of dealing with these problems by the evolution of
		  for-loops of increasing semantic complexity. We have chosen
		  two problems - a modified Santa Fe ant problem and a
		  sorting problem - which have natural looping constructs in
		  their solution and a solution without loops is not possible
		  unless the tree depth is very large. We have shown that by
		  controlling the complexity of the loop structures it is
		  possible to evolve smaller and more understandable programs
		  for these problems.}
}
 
Manuel Clavel, Francisco Durán, Steven Eker, Patrick Lincoln, Narciso Martí-Oliet, , José Meseguer, and Carolyn Talcott. The maude 2.0 system. In R. Nieuwenhuis, editor, Rewriting Techniques and Applications. 14th International Conference, RTA'03, Valencia, Spain, June9-11, 2003. Proceedings, volume 2706 of Lecture Notes in Computer Science, pages 76-87, Berlin/Heidelberg, 2003. Springer.
@inproceedings{clavel_ea:2003,
  author = {Manuel Clavel and Francisco Dur\'{a}n and Steven Eker and
		  Patrick Lincoln and Narciso Mart\'{i}-Oliet and and
		  Jos\'{e} Meseguer and Carolyn Talcott},
  title = {The Maude 2.0 System},
  editor = {R. Nieuwenhuis},
  booktitle = {Rewriting Techniques and Applications. 14th International
		  Conference, {RTA'03}, Valencia, Spain, June\,9--11, 2003.
		  Proceedings},
  year = 2003,
  series = {Lecture Notes in Computer Science},
  volume = 2706,
  pages = {76--87},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  keywords = {algebraic specification; programming; programming
		  language; specification; term rewriting},
  abstract = {This paper gives an overview of the Maude 2.0 system. We
		  emphasize the full generality with which rewriting logic
		  and membership equational logic are supported, operational
		  semantics issues, the new built-in modules, the more
		  general Full Maude modulealgebra, the new META-LEVEL
		  module, the LTL model checker, and new implementation
		  techniques yielding substantial performance improvements in
		  rewritingmodulo. We also comment on Maude's formal tool
		  environment and on applications.},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-40254-1},
  url = {http://www.springerlink.com/content/w605166t047792j4/},
  doi = {10.1007/3-540-44881-0_7}
}
 
William W. Cohen. Pac-learning a restricted class of recursive logic programs. In AAAI'93: Proceedings of the 11th National Conference on Artificial Intelligence (Washington, DC, USA, July11-15, 1993), pages 86-92. The AAAI Press/The MIT Press, 1993.
@inproceedings{cohen:1993,
  author = {William W. Cohen},
  title = {Pac-Learning a Restricted Class of Recursive Logic
		  Programs},
  booktitle = {{AAAI'93}: Proceedings of the 11th National Conference on
		  Artificial Intelligence (Washington, DC, USA, July\,11--15,
		  1993)},
  year = 1993,
  pages = {86--92},
  publisher = {The AAAI Press\,/\,The MIT Press},
  keywords = {FORCE2; ilp; inductive programming; ip-system;
		  learnability; program synthesis; recursion}
}
 
William W. Cohen. Pac-learning recursive logic programs: Negative results. Journal of Artificial Intelligence Research, pages 541-573, 1995.
@article{cohen:1995,
  author = {William W. Cohen},
  title = {Pac-Learning Recursive Logic Programs: Negative Results},
  journal = {Journal of Artificial Intelligence Research},
  year = 1995,
  pages = {541--573},
  url = {http://www.jair.org/vol/vol2.html},
  doi = {10.1613/jair.1917},
  keywords = {ilp; induction; inductive programming; learnability;
		  pac-learning; program synthesis},
  abstract = {In a companion paper it was shown that the class of
		  constant-depth determinate k-ary recursive clauses is
		  efficiently learnable. In this paper we present negative
		  results showing that any natural generalization of this
		  class is hard to learn in Valiant's model of
		  pac-learnability. In particular, we show that the following
		  program classes are cryptographically hard to learn:
		  programs with an unbounded number of constant-depth linear
		  recursive clauses; programs with one constant-depth
		  determinate clause containing an unbounded number of
		  recursive calls; and programs with one linear recursive
		  clause of constant locality. These results immediately
		  imply the non-learnability of any more general class of
		  programs. We also show that learning a constant-depth
		  determinate program with either two linear recursive
		  clauses or one linear recursive clause and one
		  non-recursive clause is as hard as learning boolean DNF.
		  Together with positive results from the companion paper,
		  these negative results establish a boundary of efficient
		  learnability for recursive function-free clauses.}
}
 
William W. Cohen. Pac-learning recursive logic programs: Efficient algorithms. Journal of Artificial Intelligence Research, 2:501-539, 1995.
@article{cohen:1995b,
  author = {William W. Cohen},
  title = {Pac-Learning Recursive Logic Programs: Efficient
		  Algorithms},
  journal = {Journal of Artificial Intelligence Research},
  year = 1995,
  volume = 2,
  pages = {501--539},
  url = {http://www.jair.org/vol/vol2.html},
  doi = {10.1613/jair.97},
  keywords = {ilp; induction; inductive programming; learnability;
		  pac-learning; program synthesis},
  abstract = {We present algorithms that learn certain classes of
		  function-free recursive logic programs in polynomial time
		  from equivalence queries. In particular, we show that a
		  single k-ary recursive constant-depth determinate clause is
		  learnable. Two-clause programs consisting of one learnable
		  recursive clause and one constant-depth determinate
		  non-recursive clause are also learnable, if an additional
		  ``basecase'' oracle is assumed. These results immediately
		  imply the pac-learnability of these classes. Although these
		  classes of learnable recursive programs are very
		  constrained, it is shown in a companion paper that they are
		  maximally general, in that generalizing either class in any
		  natural way leads to a computationally difficult learning
		  problem. Thus, taken together with its companion paper,
		  this paper establishes a boundary of efficient learnability
		  for recursive logic programs.}
}
 
Darrell Conklin and Ian H. Witten. Complexity-based induction. Machine Learning, 16(3):203-225, September 1994.
@article{conklin/witten:1994,
  author = {Darrell Conklin and Ian H. Witten},
  title = {Complexity-based Induction},
  journal = {Machine Learning},
  year = 1994,
  volume = 16,
  number = 3,
  pages = {203--225},
  month = {September},
  abstract = {A central problem in inductive logic programming is theory
		  evaluation. Without some sort of preference criterion, any
		  two theories that explain a set of examples are equally
		  acceptable. This paper presents a scheme for evaluating
		  alternative inductive theories based on an objective
		  preference criterion. It strives to extract maximal
		  redundancy from examples, transforming structure into
		  randomness. A major strength of the method is its
		  application to learning problems where negative examples of
		  concepts are scarce or unavailable. A new measure called
		  model complexity is introduced, and its use is illustrated
		  and compared with a proof complexity measure on relational
		  learning tasks. The complementarity of model and proof
		  complexity parallels that of model and proof-theoretic
		  semantics. Model complexity, where applicable, seems to be
		  an appropriate measure for evaluating inductive logic
		  theories.},
  publisher = {Springer},
  address = {Netherlands},
  issn = {0885-6125 (Print) 1573-0565 (Online)},
  url = {http://www.springerlink.com/content/j8732046806714x4/},
  doi = {10.1023/A:1022641209111},
  keywords = {Inductive logic programming; data compression; minimum
		  description length principle; model complexity; learning
		  from positive–only examples; theory preference
		  criterion}
}
 
Bruno Courcelle. Infinite trees in normal form and recursive equations having a unique solution. Theory of Computing Systems, 13(1):131-180, December 1979.
@article{courcelle:1979,
  author = {Bruno Courcelle},
  title = {Infinite Trees in Normal Form and Recursive Equations
		  having a Unique Solution},
  journal = {Theory of Computing Systems},
  year = 1979,
  volume = 13,
  number = 1,
  pages = {131--180},
  month = {December},
  publisher = {Springer},
  keywords = {recursive program schemes; semantics},
  abstract = {A system of recursive equations isC-univocal if it has a
		  unique solution modulo the equivalence associated with a
		  classC of interpretations. This concept yields simplified
		  proofs of equivalence of recursive program schemes and
		  correctness criteria for the validity of certain program
		  transformations, provided one has syntactic easily testable
		  conditions forC-univocality. Such conditions are given for
		  equational classes of interpretations.},
  address = {New York},
  issn = {1432-4350 (Print) 1433-0490 (Online)},
  url = {http://www.springerlink.com/content/u46w366127154368/},
  doi = {10.1007/BF01744293}
}
 
Bruno Courcelle. Recursive applicative program schemes. In Handbook of Theoretical Computer Science: Formal Models and Semantics, volume B, chapter 9, pages 459-492. MIT Press, Cambridge, MA, USA, 1990.
@incollection{courcelle:1990,
  author = {Bruno Courcelle},
  title = {Recursive Applicative Program Schemes},
  booktitle = {Handbook of Theoretical Computer Science: Formal Models
		  and Semantics},
  publisher = {MIT Press},
  year = 1990,
  volume = {B},
  chapter = 9,
  pages = {459--492},
  address = {Cambridge, MA, USA},
  url = {http://portal.acm.org/citation.cfm?id=114891.114900},
  keywords = {recursive program schemes; semantics}
}
 
Michael A. Covington. Natural Language Processing for Prolog Programmers. Prentice Hall, Upper Saddle River, NJ, USA, 1993.
@book{covington:1993,
  author = {Michael A. Covington},
  title = {Natural Language Processing for Prolog Programmers},
  publisher = {Prentice Hall},
  year = 1993,
  address = {Upper Saddle River, NJ, USA},
  isbn = 0136292135,
  keywords = {nlp}
}
 
Neil Crossley, Emanuel Kitzelmann, Martin Hofmann, and Ute Schmid. Combining analytical and evolutionary inductive programming. In B. Goertzel, P. Hitzler, and M. Hutter, editors, Artificial General Intelligence. AGI'09: Proceedings of the 2nd Conference on Artificial General Intelligence (Arlington, Virginia, March6-9, 2009), Advances in Intelligent Systems Research, pages 19-24. Atlantis Press, 2009.
@inproceedings{crossley_ea:2009,
  author = {Neil Crossley and Emanuel Kitzelmann and Martin Hofmann
		  and Ute Schmid},
  title = {Combining Analytical and Evolutionary Inductive
		  Programming},
  editor = {B. Goertzel and P. Hitzler and M. Hutter},
  booktitle = {Artificial General Intelligence. {AGI'09}: Proceedings of
		  the 2nd Conference on Artificial General Intelligence
		  (Arlington, Virginia, March\,6--9, 2009)},
  year = 2009,
  series = {Advances in Intelligent Systems Research},
  pages = {19--24},
  publisher = {Atlantis Press},
  isbn = {978-90-78677-24-6},
  url = {http://dx.doi.org/10.2991/agi.2009.1},
  keywords = {inductive programming},
  abstract = {Analytical inductive programming and evolutionary
		  inductive programming are two opposing strategies for
		  learning recursive programs from incomplete specifications
		  such as input/output examples. Analytical inductive
		  programming is data-driven, namely, the minimal recursive
		  generalization over the positive input/output examples is
		  generated by recurrence detection. Evolutionary inductive
		  programming, on the other hand, is based on searching
		  through hypothesis space for a (recursive) program which
		  performs sufficiently well on the given input/output
		  examples with respect to some measure of fitness. While
		  analytical approaches are fast and guarantee some
		  characteristics of the induced program by construction
		  (such as minimality and termination) the class of inducable
		  programs is restricted to problems which can be specified
		  by few positive examples. The scope of programs which can
		  be generated by evolutionary approaches is, in principle,
		  unrestricted, but generation times are typically high and
		  there is no guarantee that such a program is found for
		  which the fitness is optimal. We present a first study
		  exploring possible benefits from combining analytical and
		  evolutionary inductive programming. We use the analytical
		  system Igor2 to generate skeleton programs which are used
		  as initial hypotheses for the evolutionary system Adate. We
		  can show that providing such constraints can reduce the
		  induction time of Adate.}
}
 
Neil Crossley, Emanuel Kitzelmann, Martin Hofmann, and Ute Schmid. Evolutionary Programming Guided by Analytically Generated Seeds. In António Dourado, Agostinho C. Rosa, and Kurosh Madani, editors, IJCCI'09: Proceedings of the International Joint Conference on Computational Intelligence (Valencia, Spain, Oct.24-26, 2009), pages 198-203. INSTICC Press, 2009.
@inproceedings{crossley_ea:2009b,
  author = {Neil Crossley and Emanuel Kitzelmann and Martin Hofmann
		  and Ute Schmid},
  title = {{Evolutionary Programming Guided by Analytically Generated
		  Seeds}},
  editor = {Ant{\'o}nio Dourado and Agostinho C. Rosa and Kurosh
		  Madani},
  booktitle = {{IJCCI'09}: Proceedings of the International Joint
		  Conference on Computational Intelligence (Valencia, Spain,
		  Oct.\,24--26, 2009)},
  year = 2009,
  pages = {198--203},
  publisher = {INSTICC Press},
  isbn = {978-989-674-014-6}
}
 
James Cussens and Alan M. Frisch, editors. Inductive Logic Programming, 10th International Conference, ILP'00, London, UK, July24-27, 2000. Proceedings, volume 1866 of Lecture Notes in Computer Science, Berlin/Heidelberg, 2000. Springer.
@proceedings{cussens/frisch:2000,
  title = {Inductive Logic Programming, 10th International
		  Conference, {ILP'00}, London, UK, July\,24--27, 2000.
		  Proceedings},
  year = 2000,
  editor = {James Cussens and Alan M. Frisch},
  volume = 1866,
  series = {Lecture Notes in Computer Science},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-67795-6},
  url = {http://www.springerlink.com/content/mtr4c2ntyngw/},
  doi = {10.1007/3-540-44960-4}
}
 
Allen Cypher. Programming repetitive tasks by demonstration. In Allen Cypher, editor, Watch What I Do: Programming by Demonstration, pages 205-217. The MIT Press, 1993.
@incollection{cypher:1993,
  author = {Allen Cypher},
  title = {Programming repetitive tasks by demonstration},
  editor = {Allen Cypher},
  booktitle = {Watch What I Do: Programming by Demonstration},
  publisher = {The MIT Press},
  year = 1993,
  pages = {205--217}
}
 
Allen Cypher, editor. Watch What I Do: Programming by Demonstration. The MIT Press, 1993.
@book{cypher:1993b,
  editor = {Allen Cypher},
  title = {Watch What I Do: Programming by Demonstration},
  publisher = {The MIT Press},
  year = 1993
}
 
Luc De Raedt and Luc Dehaspe. Clausal discovery. Machine Learning, 26(2-3):99-146, February 1997.
@article{de-raedt/dehaspe:1997,
  author = {De~Raedt, Luc and Dehaspe, Luc},
  title = {Clausal Discovery},
  journal = {Machine Learning},
  year = 1997,
  volume = 26,
  number = {2-3},
  pages = {99--146},
  month = {February},
  publisher = {Springer Netherlands},
  issn = {0885-6125 (Print) 1573-0565 (Online)},
  url = {http://www.springerlink.com/content/j30702810h758166/},
  doi = {10.1023/A:1007361123060},
  keywords = {Inductive Logic Programming; Knowledge Discovery in
		  Databases; Data Mining; Learning; Induction; Semantics for
		  Induction; Logic of Induction; Parallel Learning},
  abstract = {The clausal discovery engine claudien is presented.
		  CLAUDIEN is an inductive logic programming engine that fits
		  in the descriptive data mining paradigm. CLAUDIEN addresses
		  characteristic induction from interpretations, a task which
		  is related to existing formalisations of induction in
		  logic. In characteristic induction from interpretations,
		  the regularities are represented by clausal theories, and
		  the data using Herbrand interpretations. Because CLAUDIEN
		  uses clausal logic to represent hypotheses, the
		  regularities induced typically involve multiple relations
		  or predicates. CLAUDIEN also employs a novel declarative
		  bias mechanism to define the set of clauses that may appear
		  in a hypothesis.}
}
 
Luc De Raedt, editor. Advances in Inductive Logic Programming. IOS Press, 1996.
@book{de-raedt:1996,
  editor = {De~Raedt, Luc},
  title = {Advances in Inductive Logic Programming},
  publisher = {IOS Press},
  year = 1996,
  url = {http://books.google.de/books?id=GGN6jPacVy0C},
  keywords = {book; ilp; induction; machine learning},
  annote = {contains, amongst others, several contributions to the
		  learning of recursive logic programs, thus interesting for
		  inductive programming research}
}
 
Luc De Raedt. Logical settings for concept-learning. Artificial Intelligence, 95(1):187-201, 1997.
@article{de-raedt:1997,
  author = {De~Raedt, Luc},
  title = {Logical Settings for Concept-Learning},
  journal = {Artificial Intelligence},
  year = 1997,
  volume = 95,
  number = 1,
  pages = {187--201},
  address = {Essex, UK},
  publisher = {Elsevier Science Publishers Ltd.},
  url = {http://dx.doi.org/10.1016/S0004-3702(97)00041-6},
  keywords = {ilp; learnability},
  abstract = {Three different formalizations of concept-learning in
		  logic (as well as some variants) are analyzed and related.
		  It is shown that learning from interpretations reduces to
		  learning from entailment, which in turn reduces to learning
		  from satisfiability. The implications of this result for
		  inductive logic programming and computational learning
		  theory are then discussed, and guidelines for choosing a
		  problem-setting are formulated.}
}
 
Nachum Dershowitz. Programming by analogy. In Ryszard S. Michalski, Jaime G. Carbonell, and Tom M. Mitchell, editors, Machine Learning. An Artificial Intelligence Approach, volume 2, chapter 15, pages 393-422. Morgan Kaufmann, Los Altos, CA, 1986.
@incollection{dershowitz:1986,
  author = {Nachum Dershowitz},
  title = {Programming by analogy},
  editor = {Ryszard S. Michalski and Jaime G. Carbonell and Tom M.
		  Mitchell},
  booktitle = {Machine Learning. An Artificial Intelligence Approach},
  publisher = {Morgan Kaufmann},
  year = 1986,
  volume = 2,
  chapter = 15,
  pages = {393--422},
  address = {Los Altos, CA}
}
 
Yves Deville and Kung-Kiu Lau. Logic program synthesis. Journal of Logic Programming, 1994.
@article{deville/lau:1994,
  author = {Yves Deville and Kung-Kiu Lau},
  title = {Logic Program Synthesis},
  journal = {Journal of Logic Programming},
  year = 1994,
  keywords = {deductive program synthesis; ilp; inductive programming;
		  program synthesis; survey}
}
 
Surnjani Djoko, Diane J. Cook, and Lawrence B. Holder. An empirical study of domain knowledge and its benefits to substructure discovery. IEEE Transactions on Knowledge and Data Engineering, 9(4):575-586, 1997.
@article{djoko_ea:1997,
  author = {Surnjani Djoko and Diane J. Cook and Lawrence B. Holder},
  title = {An Empirical Study of Domain Knowledge and Its Benefits to
		  Substructure Discovery},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = 1997,
  volume = 9,
  number = 4,
  pages = {575--586},
  address = {Piscataway, NJ, USA},
  url = {http://dx.doi.org/10.1109/69.617051},
  issn = {1041-4347},
  publisher = {IEEE Educational Activities Department},
  abstract = {Discovering repetitive, interesting, and functional
		  substructures in a structural database improves the ability
		  to interpret and compress the data. However, scientists
		  working with a database in their area of expertise often
		  search for predetermined types of structures or for
		  structures exhibiting characteristics specific to the
		  domain. The paper presents a method for guiding the
		  discovery process with domain specific knowledge. The
		  SUBDUE discovery system is used to evaluate the benefits of
		  using domain knowledge to guide the discovery process.
		  Domain knowledge is incorporated into SUBDUE following a
		  single general methodology to guide the discovery process.
		  Results show that domain specific knowledge improves the
		  search for substructures that are useful to the domain and
		  leads to greater compression of the data.}
}
 
Martin Dostál. On evolving of recursive functions using lambda abstraction and higher-order functions. Logic Journal of IGPL, 13(5):515-524, 2005.
@article{dostal:2005,
  author = {Martin Dost\'{a}l},
  title = {On Evolving of Recursive Functions Using Lambda
		  Abstraction and Higher-order Functions},
  journal = {Logic Journal of IGPL},
  year = 2005,
  volume = 13,
  number = 5,
  pages = { 515--524},
  publisher = {Oxford University Press},
  url = {http://jigpal.oxfordjournals.org/cgi/gca?sendit=Get+All+Checked+Abstract(s)&gca=13%2F5%2F515},
  keywords = {enumerative ip; gp; higher-order functions; ifp;
		  induction; inductive programming; program evolution;
		  program synthesis}
}
 
Martin Dostál. A functional approach to evolving recursive programs. In Emanuel Kitzelmann and Ute Schmid, editors, AAIP'07: Proceedings of the 2nd Workshop on Approaches and Applications of Inductive Programming (Warsaw, Poland, September17, 2007), pages 27-38, 2007. Work in Progress Report.
@inproceedings{dostal:2007,
  author = {Martin Dost{\'a}l},
  title = {A Functional Approach to Evolving Recursive Programs},
  editor = {Emanuel Kitzelmann and Ute Schmid},
  booktitle = {{AAIP'07}: Proceedings of the 2nd Workshop on Approaches
		  and Applications of Inductive Programming (Warsaw, Poland,
		  September\,17, 2007)},
  year = 2007,
  pages = {27--38},
  note = {Work in Progress Report},
  url = {http://www.cogsys.wiai.uni-bamberg.de/aaip07/},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip07/aaip_print.pdf},
  keywords = {enumerative ip; gp; higher-order functions; induction;
		  inductive programming; program evolution; program
		  synthesis}
}
 
Marko C. J. D. van Eekelen, editor. TFP'05: Revised Selected Papers from the 6th Symposium on Trends in Functional Programming (Tallinn, Estonia, Sep.23-24, 2005), volume 6 of Trends in Functional Programming. Intellect, 2007.
@proceedings{eekelen:2007,
  title = {{TFP'05}: Revised Selected Papers from the 6th Symposium
		  on Trends in Functional Programming (Tallinn, Estonia,
		  Sep.\,23--24, 2005)},
  year = 2007,
  editor = {Marko C. J. D. van Eekelen},
  volume = 6,
  series = {Trends in Functional Programming},
  publisher = {Intellect},
  isbn = {978-1-84150-176-5}
}
 
Hartmut Ehrig and Bernd Mahr. Fundamentals of Algebraic Specification 1. Springer, 1985.
@book{ehrig/mahr:1985,
  author = {Hartmut Ehrig and Bernd Mahr},
  title = {Fundamentals of Algebraic Specification 1},
  publisher = {Springer},
  year = 1985,
  keywords = {algebraic specification}
}
 
Esra Erdem and Pierre Flener. A redefinition of least generalizations and its application to inductive logic program synthesis. Technical report, unknown, 1997.
@techreport{erdem/flener:1997,
  author = {Erdem, Esra and Flener, Pierre},
  title = {{A redefinition of least generalizations and its
		  application to inductive logic program synthesis}},
  institution = {unknown},
  year = 1997,
  documenturl = {http://www.cs.bilkent.edu.tr/tech-reports/1997/BU-CEIS-9718.ps.z}
}
 
Esra Erdem and Pierre Flener. Completing open logic programs by constructive induction. International Journal of Intelligent Systems, 14(10):995-1019, 1999.
@article{erdem/flener:1999,
  author = {Esra Erdem and Pierre Flener},
  title = {Completing open logic programs by constructive induction},
  journal = {International Journal of Intelligent Systems},
  year = 1999,
  volume = 14,
  number = 10,
  pages = {995--1019},
  address = {Department of Computer Sciences, The University of Texas
		  at Austin, Austin, Texas 78712, USA; Department of
		  Information Science, Uppsala University, Box 311, S-751 05
		  Uppsala, Sweden},
  publisher = {John Wiley & Sons},
  url = {10.1002/(SICI)1098-111X(199910)14:10<995::AID-INT4>3.0.CO;2-W},
  annote = {Wiley InterScience: Journal: Abstract}
}
 
Floriana Esposito, Donato Malerba, and Francesca A. Lisi. Induction of recursive theories in the normal ilp setting: Issues and solutions. In James Cussens and Alan M. Frisch, editors, Inductive Logic Programming. 10th International Conference, ILP'00, London, UK, July24-27, 2000, Proceedings, volume 1866 of Lecture Notes in Computer Science, pages 93-111, Berlin/Heidelberg, 2000. Springer.
@inproceedings{esposito_ea:2000,
  author = {Floriana Esposito and Donato Malerba and Francesca A.
		  Lisi},
  title = {Induction of Recursive Theories in the Normal ILP Setting:
		  Issues and Solutions},
  editor = {James Cussens and Alan M. Frisch},
  booktitle = {Inductive Logic Programming. 10th International
		  Conference, {ILP'00}, London, UK, July\,24--27, 2000,
		  Proceedings},
  year = 2000,
  series = {Lecture Notes in Computer Science},
  volume = 1866,
  pages = {93--111},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-67795-6},
  url = {http://www.springerlink.com/content/vrmwvmc9dfv108re/},
  doi = {10.1007/3-540-44960-4_6},
  keywords = {ATRE; ilp; inductive programming; ip-system; program
		  synthesis; recursion},
  abstract = {Induction of recursive theories in the normal ILP setting
		  is a complex task because of the non-monotonicity of the
		  consistency property. In this paper we propose
		  computational solutions to some relevant issues raised by
		  the multiple predicate learningproblem. A
		  separate-and-parallel-conquer search strategy is adopted to
		  interleave the learning of clauses supplying predicateswith
		  mutually recursive definitions. A novel generality order to
		  be imposed to the search space of clauses is investigatedin
		  order to cope with recursion in a more suitable way. The
		  consistency recovery is performed by reformulating the
		  currenttheory and by applying a layering technique based on
		  the collapsed dependency graph. The proposed approach has
		  been implementedin the ILP system ATRE and tested in the
		  specific context of the document understanding problem
		  within the WISDOM project.Experimental results are
		  discussed and future directions are drawn.}
}
 
V. Estruch, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. Generalisation operators for lists embedded in a metric space. In Ute Schmid, Emanuel Kitzelmann, and Rinus Plasmeijer, editors, Approaches and Applications of Inductive Programming. 3rd International Workshop, AAIP'09, Edinburgh, UK, Sept.4, 2009. Revised Papers, volume 5812 of Lecture Notes in Computer Science, pages 117-139, Berlin/Heidelberg, 2010. Springer.
@inproceedings{estruch_ea:2010,
  author = {V. Estruch and C. Ferri and J. Hern\'andez-Orallo and M.
		  J. Ram\'{\i}rez-Quintana},
  title = {Generalisation Operators for Lists Embedded in a Metric
		  Space},
  editor = {Ute Schmid and Emanuel Kitzelmann and Rinus Plasmeijer},
  booktitle = {Approaches and Applications of Inductive Programming. 3rd
		  International Workshop, {AAIP'09}, Edinburgh, UK, Sept.\,4,
		  2009. Revised Papers},
  year = 2010,
  series = {Lecture Notes in Computer Science},
  volume = 5812,
  pages = {117--139},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  documenturl = {http://www.springerlink.com/content/0151147952k37k54/fulltext.pdf},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-11930-9},
  url = {http://www.springerlink.com/content/0151147952k37k54/},
  abstract = {In some application areas, similarities and distances are
		  used to calculate how similar two objects are in order to
		  use these measurements to find related objects, to cluster
		  a set of objects, to make classifications or to perform an
		  approximate search guided by the distance. In many other
		  application areas, we require patterns to describe
		  similarities in the data. These patterns are usually
		  constructed through generalisation (or specialisation)
		  operators. For every data structure, we can define
		  distances. In fact, we may find different distances for
		  sets, lists, atoms, numbers, ontologies, web pages, etc. We
		  can also define pattern languages and use generalisation
		  operators over them. However, for many data structures,
		  distances and generalisation operators are not consistent.
		  For instance, for lists (or sequences), edit distances are
		  not consistent with regular languages, since, for a regular
		  pattern such as *},
  keywords = {Distance-based methods; inductive operators; induction
		  with distances; list-based representations},
  doi = {10.1007/978-3-642-11931-6_6}
}
 
C. Ferri-Ramírez, José Hernández-Orallo, and M. José Ramírez-Quintana. Incremental learning of functional logic programs. In Functional and Logic Programming. 5th International Symposium, FLOPS'01, Tokyo, Japan, March7-9, 2001. Proceedings, volume 2024 of Lecture Notes in Computer Science, pages 233-247, Berlin/Heidelberg, 2001. Springer.
@inproceedings{ferri-ramirez_ea:2001,
  author = {C. Ferri-Ram{\'i}rez and Jos{\'e} Hern{\'a}ndez-Orallo and
		  M. Jos{\'e} Ram{\'i}rez-Quintana},
  title = {Incremental Learning of Functional Logic Programs},
  booktitle = {Functional and Logic Programming. 5th International
		  Symposium, {FLOPS'01}, Tokyo, Japan, March\,7--9, 2001.
		  Proceedings},
  year = 2001,
  series = {Lecture Notes in Computer Science},
  volume = 2024,
  pages = {233--247},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-41739-2},
  url = {http://www.springerlink.com/content/9kmf4guqdx8v8367/},
  keywords = {flip; iflp; inductive programming; ip-system; program
		  synthesis; recursion; Inductive functional logic
		  programming (IFLP); inductive logic programming (ILP);
		  incremental learning; theory revision},
  doi = {10.1007/3-540-44716-4_15},
  abstract = {In this work, we consider the extension of the Inductive
		  Functional Logic Programming (IFLP) framework in order to
		  learn functions in an incremental way. In general,
		  incremental learning is necessary when the number of
		  examples is infinite, very large orpresented one by one. We
		  have performed this extension in the FLIP system, an
		  implementation of the IFLP framework. Severalexamples of
		  programs which have been induced indicate that our
		  extension pays off in practice. An experimental study of
		  someparameters which affect this efficiency is performed
		  and some applications for programming practice are
		  illustrated, especiallysmall classification problems and
		  data-mining of semi-structured data.}
}
 
Pierre Flener and Yves Deville. Logic program transformation through generalization schemata. In Logic Program Synthesis and Transformation. 5th International Workshop, LOPSTR'95, Utrecht, The Netherlands, Sept.20-22, 1995. Proceedings, volume 1048 of Lecture Notes in Computer Science, pages 171-173, Berlin/Heidelberg, 1996. Springer.
@inproceedings{flener/deville:1996,
  author = {Pierre Flener and Yves Deville},
  title = {Logic Program Transformation through Generalization
		  Schemata},
  booktitle = {Logic Program Synthesis and Transformation. 5th
		  International Workshop, {LOPSTR'95}, Utrecht, The
		  Netherlands, Sept.\,20--22, 1995. Proceedings},
  year = 1996,
  series = {Lecture Notes in Computer Science},
  volume = 1048,
  pages = {171--173},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-60939-1},
  url = {http://www.springerlink.com/content/f46v3640937r88g4/},
  abstract = {Both generalization techniques are very suitable for
		  mechanical transformation: all operators of the generalized
		  programs are operators of the initial programs. Given a
		  divide-and-conquer program, a mere inspection of the
		  properties of its solving, processing, and composition
		  operators thus allows the detection of which kinds of
		  generalization are possible, and to which optimizations
		  they would lead. Theeurekadiscoveries are compiled away,
		  and the transformations can be completely automated.},
  doi = {10.1007/3-540-60939-3_13},
  keywords = {1995; Deville; Dialogs; Flener; inductive programming;
		  inproceedings; logic programming},
  annote = {Logic Program Transformation through Generalization
		  Schemata - Flener, Deville (ResearchIndex)}
}
 
Pierre Flener and Derek Partridge. Inductive programming. Automated Software Engineering, 8(2):131-137, April 2001.
@article{flener/partridge:2001,
  author = {Pierre Flener and Derek Partridge},
  title = {Inductive Programming},
  journal = {Automated Software Engineering},
  year = 2001,
  volume = 8,
  number = 2,
  pages = {131--137},
  month = {April},
  url = {http://dx.doi.org/10.1023/A:1008797606116},
  keywords = {article; ase; induction; inductive programming; position
		  paper; program synthesis; software engineering}
}
 
Pierre Flener and Lubos Popelinsky. On the use of inductive reasoning in program synthesis: Prejudice and prospects. In L. Fribourg and F. Turini, editors, Logic Program Synthesis and Transformation - Meta-Programming in Logic. 4th International Workshops, LOPSTR'94 and META'94, Pisa, Italy, June20-21, 1994. Proceedings, volume 883 of Lecture Notes in Computer Science, pages 69-87, Berlin/Heidelberg, 1994. Springer.
@inproceedings{flener/popelinsky:1994,
  author = {Pierre Flener and Lubos Popelinsky},
  title = {On the Use of Inductive Reasoning in Program Synthesis:
		  Prejudice and Prospects},
  editor = {L. Fribourg and F. Turini},
  booktitle = {Logic Program Synthesis and Transformation ---
		  Meta-Programming in Logic. 4th International Workshops,
		  {LOPSTR'94} and {META'94}, Pisa, Italy, June\,20--21, 1994.
		  Proceedings},
  year = 1994,
  series = {Lecture Notes in Computer Science},
  volume = 883,
  pages = {69--87},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-58792-7},
  url = {http://www.springerlink.com/content/y739v33lt5261p5p/},
  doi = {10.1007/3-540-58792-6_5},
  keywords = {ilp; inductive programming; inproceedings; position paper;
		  program synthesis; software engineering},
  abstract = {In this position paper, we give a critical analysis of the
		  deductive and inductive approaches to program synthesis,
		  and of the current research in these fields. From the
		  shortcomings of these approaches and works, we identify
		  future research directions for these fields, as well as a
		  need for cooperation and cross-fertilization between
		  them.}
}
 
Pierre Flener and Ute Schmid. An introduction to inductive programming. Artificial Intelligence Review, 29(1):45-62, 2008.
@article{flener/schmid:2008,
  author = {Pierre Flener and Ute Schmid},
  title = {An Introduction to Inductive Programming},
  journal = {Artificial Intelligence Review},
  year = 2008,
  volume = 29,
  number = 1,
  pages = {45--62},
  keywords = {inductive programming}
}
 
Pierre Flener and Serap Yilmaz. Inductive synthesis of recursive logic programs: Achievements and prospects. The Journal of Logic Programming, 41(2-3):141-195, November/December 1999.
@article{flener/yilmaz:1999,
  author = {Pierre Flener and Serap Yilmaz},
  title = {Inductive Synthesis of Recursive Logic Programs:
		  Achievements and Prospects},
  journal = {The Journal of Logic Programming},
  year = 1999,
  volume = 41,
  number = {2--3},
  pages = {141--195},
  month = {November\,/\,December},
  annote = {Contains an overview of ilp systems for program
		  synthesis.},
  url = {http://dx.doi.org/10.1016/S0743-1066(99)00028-X},
  keywords = {comparison; dialogs; ilp; inductive programming;
		  ip-system; program synthesis; recursion; survey},
  abstract = {The inductive synthesis of recursive logic programs from
		  incomplete information, such as input/output examples, is a
		  challenging subfield both of Inductive Logic Programming
		  (ILP) and of the synthesis (in general) of logic programs,
		  from formal specifications. We first overview past and
		  present achievements, focusing on the techniques that were
		  designed specifically for the inductive synthesis of
		  recursive logic programs but also discussing a few general
		  ILP techniques that can also induce non-recursive
		  hypotheses. Then we analyse the prospects of these
		  techniques in this task, investigating their applicability
		  to software engineering as well as to knowledge acquisition
		  and discovery.}
}
 
Pierre Flener. Logic Program Synthesis from Incomplete Information. Kluwer Academic Publishers, Boston, 1995.
@book{flener:1995,
  author = {Pierre Flener},
  title = {Logic Program Synthesis from Incomplete Information},
  publisher = {Kluwer Academic Publishers},
  year = 1995,
  address = {Boston},
  keywords = {ilp; inductive programming; program synthesis}
}
 
Pierre Flener. Inductive logic program synthesis with DIALOGS. In Stephen H. Muggleton, editor, ILP'96: Proceedings of the 6th International Workshop on Inductive Logic Programming (Stockholm, Sweden, Aug.26-28, 1996), pages 28-51. Stockholm University, Royal Institute of Technology, 1996.
@inproceedings{flener:1996,
  author = {Pierre Flener},
  title = {Inductive logic program synthesis with {DIALOGS}},
  editor = {Stephen H. Muggleton},
  booktitle = {{ILP'96}: Proceedings of the 6th International Workshop on
		  Inductive Logic Programming (Stockholm, Sweden,
		  Aug.\,26--28, 1996)},
  year = 1996,
  pages = {28--51},
  publisher = {Stockholm University, Royal Institute of Technology},
  keywords = {1996; Dialogs; Flener; inductive logic programming;
		  inductive programming; inproceedings},
  annote = {Inductive Logic Program Synthesis with DIALOGS - Flener
		  (ResearchIndex)}
}
 
Pierre Flener. Inductive logic program synthesis with DIALOGS. In Stephen H. Muggleton, editor, Inductive Logic Programming. 6th International Workshop, ILP'96, Stockholm, Sweden, Aug.26-28, 1996. Selected Papers, volume 1314 of Lecture Notes in Computer Science, pages 175-198, Berlin/Heidelberg, 1997. Springer.
@inproceedings{flener:1997,
  author = {Pierre Flener},
  title = {Inductive Logic Program Synthesis with {DIALOGS}},
  editor = {Stephen H. Muggleton},
  booktitle = {Inductive Logic Programming. 6th International Workshop,
		  {ILP'96}, Stockholm, Sweden, Aug.\,26--28, 1996. Selected
		  Papers},
  year = 1997,
  series = {Lecture Notes in Computer Science},
  volume = 1314,
  pages = {175--198},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  keywords = {analytical ip; dialogs; ilp; inductive programming;
		  ip-system; program synthesis; recursion},
  abstract = {DIALOGS (Dialogue-based Inductive and Abductive LOGic
		  program Synthesizer) is a schema-guided synthesizer of
		  recursive logic programs; it takes the initiative and
		  queries a (possibly computationally naive) specifier for
		  evidence in her/his conceptual language. The specifier must
		  know the answers to such simple queries, because otherwise
		  s/he wouldn't even feel the need for the synthesized
		  program. DIALOGS can be used by any learner (including
		  itself) that detects, or merely conjectures, the necessity
		  of invention of a new predicate. Due to its foundation on a
		  powerful codification of a recursion-theory (by means of
		  the template and constraints of a divide-and-conquer
		  schema), DIALOGS needs very little evidence and is very
		  fast.},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-63494-2},
  url = {http://www.springerlink.com/content/67617138x2848145/},
  doi = {10.1007/3-540-63494-0_55}
}
 
Pierre Flener. Achievements and prospects of program synthesis. In A. C. Kakas and F. Sadri, editors, Computational Logic: Logic Programming and Beyond. Essays in Honour of Robert A. Kowalski, Part I, volume 2407 of Lecture Notes in Computer Science, pages 1-43, Berlin/Heidelberg, 2002. Springer.
@inproceedings{flener:2002,
  author = {Pierre Flener},
  title = {Achievements and prospects of program synthesis},
  editor = {A. C. Kakas and F. Sadri},
  booktitle = {Computational Logic: Logic Programming and Beyond. Essays
		  in Honour of Robert A. Kowalski, Part I},
  year = 2002,
  series = {Lecture Notes in Computer Science},
  volume = 2407,
  pages = {1--43},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-43959-2},
  url = {http://www.springerlink.com/content/uhur0dmfdp27t9ma/},
  abstract = {Program synthesis research aims at developing a program
		  that develops correct programs from specifications, with as
		  much or as little interaction as the specifier wants. I
		  overview the main achievements in deploying logic for
		  program synthesis. I also outline the prospects of such
		  research, arguing that, while the technology scales up from
		  toy programs to real-life software and to commercially
		  viable tools, computational logic will continue to be a
		  driving force behind this progress.},
  doi = {10.1007/3-540-45628-7_13}
}
 
Pierre Flener, L. Popelinsky, and O. Stepankova. Ilp and automatic programming: towards three approaches. In ILP'94: Proceedings of the 4th International Workshop on Inductive Logic Programming (Bonn, Germany, Sept.12-14, 1994), volume 237 of GMD-Studien, pages 351-364. Gesellschaft für Mathematik und Datenverarbeitung MBH, 1994.
@inproceedings{flener_ea:1994,
  author = {Pierre Flener and L. Popelinsky and O. Stepankova},
  title = {ILP and automatic programming: towards three approaches},
  booktitle = {{ILP'94}: Proceedings of the 4th International Workshop on
		  Inductive Logic Programming (Bonn, Germany, Sept.\,12--14,
		  1994)},
  year = 1994,
  series = {{GMD}-Studien},
  volume = 237,
  pages = {351--364},
  publisher = {{G}esellschaft f{\"{u}}r {M}athematik und
		  {D}atenverarbeitung {MBH}},
  annote = {ute-inflit}
}
 
Pierre Flener, Kung-Kiu Lau, and Mario Ornaghi. On correct program schemas. In Norbert E. Fuchs, editor, Logic Programming Synthesis and Transformation. 7th International Workshop, LOPSTR'97, Leuven, Belgium, July10-12, 1997. Proceedings, volume 1463 of Lecture Notes in Computer Science, pages 128-147, Berlin/Heidelberg, 1998. Springer.
@inproceedings{flener_ea:1998,
  author = {Pierre Flener and Kung-Kiu Lau and Mario Ornaghi},
  title = {On Correct Program Schemas},
  editor = {Norbert E. Fuchs},
  booktitle = {Logic Programming Synthesis and Transformation. 7th
		  International Workshop, {LOPSTR'97}, Leuven, Belgium,
		  July\,10--12, 1997. Proceedings},
  year = 1998,
  series = {Lecture Notes in Computer Science},
  volume = 1463,
  pages = {128--147},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-65074-4},
  url = {http://www.springerlink.com/content/0j0rh135a1b2r7l1/},
  abstract = {We present our work on the representation and correctness
		  of program schemas, in the context of logic program
		  synthesis. Whereas most researchers represent schemas
		  purely syntactically as higher-order expressions, we shall
		  express a schema as an open first-order theory that
		  axiomatises a problem domain, called aspecification
		  framework, containing an open program that represents the
		  template of the schema. We will show that using our
		  approach we can define a meaningful notion of correctness
		  for schemas, viz. that correct program schemas can be
		  expressed asparametricspecification frameworks containing
		  templates that aresteadfast, i.e. programs that are always
		  correct provided their open relations are computed
		  correctly.},
  subject_collection = {Computer Science},
  doi = {10.1007/3-540-49674-2_7}
}
 
Maarten M. Fokkinga. Monadic Maps and Folds for Arbitrary Datatypes. Technical Report Memoranda Inf 94-28, University of Twente, Enschede, Netherlands, June 1994.
@techreport{fokkinga:1994,
  author = {Fokkinga, Maarten M.},
  title = {{Monadic Maps and Folds for Arbitrary Datatypes}},
  institution = {University of Twente},
  year = 1994,
  number = {Memoranda Inf 94--28},
  address = {Enschede, Netherlands},
  month = {June},
  abstract = {Each datatype constructor comes equiped not only with a
		  so-called map and fold (catamorphism), as is widely known,
		  but, under some condition, also with a kind of map and fold
		  that are related to an arbitrary given monad. This result
		  follows from the preservation of initiality under lifting
		  from the category of algebras in a given category to a
		  certain other category of algebras in the Kleisli category
		  related to the monad. },
  pages = {1--23}
}
 
R. Freivalds. Inductive inference of recursive functions: qualitative theory. In J. M. Barzdinš and D. Bjorner, editors, Baltic Computer Science. Selected Papers, volume 502 of Lecture Notes in Computer Science, pages 77-110. Springer, Berlin/Heidelberg, 1991.
@incollection{freivalds:1991,
  author = {R. Freivalds},
  title = {Inductive inference of recursive functions: qualitative
		  theory},
  editor = {B{\=a}rzdi{\c{n}}{\v{s}}, J. M. and Bjorner, D.},
  booktitle = {Baltic Computer Science. Selected Papers},
  publisher = {Springer},
  year = 1991,
  volume = 502,
  series = {Lecture Notes in Computer Science},
  pages = {77--110},
  address = {Berlin\,/\,Heidelberg},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-54131-8},
  url = {http://www.springerlink.com/content/57332j2671j7p508/},
  doi = {10.1007/BFb0019359},
  annote = {ute-inflit}
}
 
Mitsue Furusawa, Nobuhiro Inuzuka, Hirohisa Seki, and Hidenori Itoh. Induction of logic programs with more than one recursive clause by analyzing saturations. In Nada Lavrač and Sašo Džeroski, editors, Inductive Logic Programming. 7th International Workshop, ILP'97, Prague, Czech Republic, Sept.17-20, 1997, Proceedings, volume 1297 of Lecture Notes in Computer Science, pages 165-172, Berlin/Heidelberg, 1997. Springer.
@inproceedings{furusawa_ea:1997,
  author = {Mitsue Furusawa and Nobuhiro Inuzuka and Hirohisa Seki and
		  Hidenori Itoh},
  title = {Induction of Logic Programs with More than One Recursive
		  Clause by Analyzing Saturations},
  editor = {Nada Lavra{\v{c}} and Sa{\v{s}}o D{\v{z}}eroski},
  booktitle = {Inductive Logic Programming. 7th International Workshop,
		  {ILP'97}, Prague, Czech Republic, Sept.\,17--20, 1997,
		  Proceedings},
  year = 1997,
  series = {Lecture Notes in Computer Science},
  volume = 1297,
  pages = {165--172},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  isbn = {978-3-540-63514-7},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  url = {http://www.springerlink.com/content/c75726877p674419/},
  doi = {10.1007/3540635149_45},
  keywords = {MRI; ilp; inductive programming; ip-system; program
		  synthesis; recursion},
  abstract = {This paper describes a bottom-up ILP algorithm called MRI,
		  which induces recursive programs with one or more recursive
		  clauses from a few of examples. It analyzes saturations
		  using path structures, which express streams of terms
		  processed by predicates and was originally introduced by
		  Identam-Almquist. We introduce extension and difference of
		  path structures. Recursive clauses can be expressed as a
		  difference among path structures. The paper also shows
		  experimental results.}
}
 
Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning: theory and practice. Morgan Kaufmann, 2004.
@book{ghallab_ea:2004,
  author = {Malik Ghallab and Dana Nau and Paolo Traverso},
  title = {Automated Planning: theory and practice},
  publisher = {Morgan Kaufmann},
  year = 2004,
  keywords = {planning}
}
 
J. Y. Girard. Une extension de l'interprétation de gödel àl'analyse, et son application à l'élimination des coupures dans l'analyse et la théorie des types. In Jens Erik Fenstad, editor, Proceedings of the 2nd Scandinavian Logic Symposium (University of Oslo, June18-20, 1970), volume 63 of Studies in logic and the foundations of mathematics, pages 63-92. North-Holland, 1971.
@inproceedings{girard:1971,
  author = {J. Y. Girard},
  title = {Une extension de l'interpr\'{e}tation de G\"{o}del
		  \`{a}l'analyse, et son application \`{a} l'\'{e}limination
		  des coupures dans l'analyse et la th\'{e}orie des types},
  editor = {Jens Erik Fenstad},
  booktitle = {Proceedings of the 2nd Scandinavian Logic Symposium
		  (University of Oslo, June\,18--20, 1970)},
  year = 1971,
  series = {Studies in logic and the foundations of mathematics},
  volume = 63,
  pages = {63--92},
  publisher = {North-Holland},
  isbn = 0720422590,
  keywords = {lambda calculus; recursion theory; seminal paper; system
		  f},
  annote = {one of the two original system f works}
}
 
Joseph A. Goguen. How to prove algebraic inductive hypotheses without induction. In 5th Conference on Automated Deduction. Les Arcs, France, July8-11, 1980, volume 87 of Lecture Notes in Computer Science, pages 356-373, Berlin/Heidelberg, 1980. Springer.
@inproceedings{goguen:1980,
  author = {Joseph A. Goguen},
  title = {How to Prove Algebraic Inductive Hypotheses without
		  Induction},
  booktitle = {5th Conference on Automated Deduction. Les Arcs, France,
		  July\,8--11, 1980},
  year = 1980,
  series = {Lecture Notes in Computer Science},
  volume = 87,
  pages = {356--373},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-10009-6},
  url = {http://www.springerlink.com/content/f61317u230575416/},
  doi = {10.1007/3-540-10009-1_27},
  keywords = {algebraic specification; equational logic; term
		  rewriting},
  abstract = {This paper proves the correctness of algebraic methods for
		  deciding the equivalence of expressions by applying rewrite
		  rules, and for proving inductive equational hypotheses
		  without using induction; it also shows that the equations
		  true in the initial algebra are just those provable by
		  structural induction. The major results generalize,
		  simplify and rigorize Musser's method for proving inductive
		  hypotheses with the Knuth-Bendix algorithm; our approach
		  uses a very general result, that (under certain conditions)
		  an equation is true iff it is consistent. Finally, we show
		  how these results can be extended to proving the
		  correctness of an implementation of one data abstraction by
		  another.}
}
 
E. Mark Gold. Language identification in the limit. Information and Control, 10(5):447-474, 1967.
@article{gold:1967,
  author = {E. Mark Gold},
  title = {Language Identification in the Limit},
  journal = {Information and Control},
  year = 1967,
  volume = 10,
  number = 5,
  pages = {447--474},
  url = {http://www.isrl.uiuc.edu/~amag/langev/paper/gold67limit.html},
  keywords = {article; identification in the limit; induction; machine
		  learning; seminal paper},
  annote = {golds seminal paper on inductive inference},
  abstract = {Language learnability has been investigated. This refers
		  to the following situation: A class of possible languages
		  is specified, together with a method of presenting
		  information to the learner about an unknown language, which
		  is to be chosen from the class. The question is now asked,
		  ``Is the information sufficient to determine which of the
		  possible languages is the unknown language?'' Many
		  definitions of learnability are possible, but only the
		  following is considered here: Time is quantized and has a
		  finite starting time. At each time the learner receives a
		  unit of information and is to make a guess as to the
		  identity of the unknown language on the basis of the
		  information received so far. This process continues
		  forever. The class of languages will be considered
		  learnable with respect to the specified method of
		  information presentation if there is an algorithm that the
		  learner can use to make his guesses, the algorithm having
		  the following property: Given any language of the class,
		  there is some finite time after which the guesses will all
		  be the same and they will be correct. In this preliminary
		  investigation, a language is taken to be a set of strings
		  on some finite alphabet. The alphabet, is the same for all
		  languages of the class. Several variations of each of the
		  following two basic methods of information presentation are
		  investigated: A text for a language generates the strings
		  of the language in any order such that every string of the
		  language occurs at. least once. An informant for a language
		  tells whether a string is in the language, and chooses the
		  strings in some order such that every string occurs at
		  least once. It was found that the class of
		  context-sensitive languages is learnable from an informant,
		  but that, not even the class of regular languages is
		  learnable from a text. }
}
 
E. Marc Gold. Complexity of automaton identification from given data. Information and Control, 37:302-320, 1978.
@article{gold:1978,
  author = {E. Marc Gold},
  title = {Complexity of automaton identification from given data},
  journal = {Information and Control},
  year = 1978,
  volume = 37,
  pages = {302--320}
}
 
Palem GopalaKrishna. Data-dependencies and learning in artificial systems. In Emanuel Kitzelmann, Roland J. Olsson, and Ute Schmid, editors, AAIP'05: Proceedings of the 1st Workshop on Approaches and Applications of Inductive Programming (Bonn, Germany, Aug.7, 2005), pages 69-78, 2005. Work in Progress Reports.
@inproceedings{gopalakrishna:2005,
  author = {Palem GopalaKrishna},
  title = {Data-dependencies and Learning in Artificial Systems },
  editor = {Emanuel Kitzelmann and Roland J. Olsson and Ute Schmid},
  booktitle = {{AAIP'05}: Proceedings of the 1st Workshop on Approaches
		  and Applications of Inductive Programming (Bonn, Germany,
		  Aug.\,7, 2005)},
  year = 2005,
  pages = {69--78},
  note = {Work in Progress Reports},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip05/proceedings/DataDependenciesInLearning.pdf}
}
 
C. C. Green and D. R. Barstow. On program synthesis knowledge. Artificial Intelligence, 10:241-279, 1978.
@article{green/barstow:1978,
  author = {C. C. Green and D. R. Barstow},
  title = {On program synthesis knowledge},
  journal = {Artificial Intelligence},
  year = 1978,
  volume = 10,
  pages = {241--279}
}
 
Masami Hagiya and T. Sakurai. Foundation of logic programming based on inductive definition. New Generation Computing, 2(1):59-77, 1984.
@article{hagiya/sakurai:1984,
  author = {Hagiya, Masami and Sakurai, T.},
  title = {Foundation of logic programming based on inductive
		  definition},
  journal = {New Generation Computing},
  year = 1984,
  volume = 2,
  number = 1,
  pages = {59--77},
  publisher = {Springer}
}
 
Masami Hagiya. Programming by example and proving by example using higher-order unification. In 10th International Conference on Automated Deduction. Kaiserslautern, FRG, July24-27, 1990. Proceedings, volume 449 of Lecture Notes in Computer Science, pages 588-602, Berlin/Heidelberg, 1990. Springer.
@inproceedings{hagiya:1990,
  author = {Hagiya, Masami},
  title = {Programming by example and proving by example using
		  higher-order unification},
  booktitle = {10th International Conference on Automated Deduction.
		  Kaiserslautern, {FRG}, July\,24--27, 1990. Proceedings},
  year = 1990,
  series = {Lecture Notes in Computer Science},
  volume = 449,
  pages = {588--602},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-52885-2},
  url = {http://www.springerlink.com/content/p4543uux655h1647/},
  doi = {10.1007/3-540-52885-7_116},
  keywords = {ifp; induction; inductive programming; inproceedings;
		  program synthesis},
  abstract = {We propose a new approach to programming by example, in
		  which a program is synthesized from examples by
		  higher-order unification in a type theory with a recursion
		  operator. The approach, when applied to the problem of
		  proof generalization, makes it possible to synthesize a
		  general proof from a concrete example proof and establish a
		  method of proving by example. Cases in which a program and
		  a proof are simultaneously synthesized are also considered.
		  In order to represent proofs as terms and generalize proof
		  terms by higher-order unification, we extend Logical
		  Framework to a system with product and equality.}
}
 
Lutz Hamel and Chi Shen. Inductive acquisition of algebraic specifications. In WADT'06: Proceedings of the Workshop for Algebraic Development Techniques (La Roche en Ardenne, Belgium, June1-3, 2006), 2006.
@inproceedings{hamel/shen:2006,
  author = {Lutz Hamel and Chi Shen},
  title = {Inductive Acquisition of Algebraic Specifications},
  booktitle = {{WADT'06}: Proceedings of the Workshop for Algebraic
		  Development Techniques (La Roche en Ardenne, Belgium,
		  June\,1--3, 2006)},
  year = 2006,
  documenturl = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.107.8027&rep=rep1&type=pdf}
}
 
Lutz Hamel and Chi Shen. An inductive programming approach to algebraic specification. In Emanuel Kitzelmann and Ute Schmid, editors, AAIP'07: Proceedings of the 2nd Workshop on Approaches and Applications of Inductive Programming (Warsaw, Poland, September17, 2007), pages 3-14, 2007. Invited Talk.
@inproceedings{hamel/shen:2007,
  author = {Lutz Hamel and Chi Shen},
  title = {An Inductive Programming Approach to Algebraic
		  Specification},
  editor = {Emanuel Kitzelmann and Ute Schmid},
  booktitle = {{AAIP'07}: Proceedings of the 2nd Workshop on Approaches
		  and Applications of Inductive Programming (Warsaw, Poland,
		  September\,17, 2007)},
  year = 2007,
  pages = {3--14},
  note = {Invited Talk},
  keywords = {algebraic specification; enumerative ip; ifp; induction;
		  inductive programming; program evolution; program
		  synthesis},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip07/aaip_print.pdf},
  url = {http://www.cogsys.wiai.uni-bamberg.de/aaip07/}
}
 
Lutz Hamel. Breeding algebraic structures - an evolutionary approach to inductive equational logic programming. In GECCO'02: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation (New York, USA, July09-13, 2002), pages 748-755, San Francisco, CA, USA, 2002. Morgan Kaufmann.
@inproceedings{hamel:2002,
  author = {Lutz Hamel},
  title = {Breeding Algebraic Structures -- An Evolutionary Approach
		  To Inductive Equational Logic Programming},
  booktitle = {{GECCO'02}: Proceedings of the 4th Annual Conference on
		  Genetic and Evolutionary Computation (New York, USA,
		  July\,09--13, 2002)},
  year = 2002,
  pages = {748--755},
  address = {San Francisco, CA, USA},
  publisher = {Morgan Kaufmann},
  isbn = {1-55860-878-8},
  url = {http://portal.acm.org/citation.cfm?id=646205.683098},
  keywords = {algebraic specification; enumerative ip; ifp; induction;
		  inductive programming; program evolution; program
		  synthesis},
  annote = {Breeding Algebraic Structures - An Evolutionary Approach
		  To Inductive Equational Logic Programming}
}
 
Andreas Hamfelt, Jørgen Fischer Nilsson, and Nikolaj Oldager. Logic program synthesis as problem reduction using combining forms. Automated Software Engineering, 8(2):167-193, 2001.
@article{hamfelt_ea:2001,
  author = {Andreas Hamfelt and J\o rgen Fischer Nilsson and Nikolaj
		  Oldager},
  title = {Logic Program Synthesis as Problem Reduction Using
		  Combining Forms},
  journal = {Automated Software Engineering},
  year = 2001,
  volume = 8,
  number = 2,
  pages = {167--193},
  publisher = {Springer Netherlands},
  issn = {0928-8910 (Print) 1573-7535 (Online)},
  url = {http://www.springerlink.com/content/g66225t50x26844k/},
  abstract = {This paper presents an approach to inductive synthesis of
		  logic programs from examples using problem decomposition
		  and problem reduction principles. This is in contrast to
		  the prevailing logic program induction paradigm, which
		  relies on generalization of programs from examples. The
		  problem reduction is accomplished as a constrained top-down
		  search process, which eventually is to reach trivial
		  problems.Our induction scheme applies a distinguished logic
		  programming language in which programs are combined from
		  elementary predicates by means of combinators conceived of
		  as problem reduction operators including list recursion
		  forms. The operator form admits inductive synthesis as a
		  top-down piecewise composition of semantically meaningful
		  program elements according to the compositional semantics
		  principle and with appeals neither to special
		  generalization mechanisms nor to alternative forms of
		  resolution and unification, or predicate invention.The
		  search space is reduced by subjecting the induction process
		  to various constraints concerning syntactical form, modes,
		  data types, and computational resources. This is
		  illustrated in the paper with well-modedness constraints
		  with the aim of synthesising well-moded, procedurally
		  acceptable programs.Preliminary experiments with the
		  proposed induction method lead us to tentatively conclude
		  that the presented approach forms a viable alternative to
		  the prevailing inductive logic programming methods applying
		  generalization from examples.},
  doi = {10.1023/A:1008741507024},
  keywords = {article; ase; combilog; combinduce; enumerative ip; ilp;
		  induction; inductive programming; program synthesis;
		  recursion schemes}
}
 
Michael Hanus, editor. Logic-Based Program Synthesis and Transformation, 18th International Symposium, LOPSTR'08, Valencia, Spain, July 17-18, 2008, Revised Selected Papers, volume 5438 of Lecture Notes in Computer Science, Berlin/Heidelberg, 2009. Springer.
@proceedings{hanus:2009,
  title = {Logic-Based Program Synthesis and Transformation, 18th
		  International Symposium, {LOPSTR'08}, Valencia, Spain, July
		  17--18, 2008, Revised Selected Papers},
  year = 2009,
  editor = {Michael Hanus},
  volume = 5438,
  series = {Lecture Notes in Computer Science},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-00514-5},
  url = {http://www.springerlink.com/content/u06956481x17/},
  doi = {10.1007/978-3-642-00515-2}
}
 
S. Hardy. Synthesis of LISP functions from examples. In IJCAI'75: Proceedings of the 4th International Joint Conference on Artificial Intelligence (Tbilisi, Georgia, USSR, Sept.3-8, 1975), pages 240-245, 1975.
@inproceedings{hardy:1975,
  author = {S. Hardy},
  title = {Synthesis of {LISP} Functions from Examples},
  booktitle = {{IJCAI}'75: Proceedings of the 4th International Joint
		  Conference on Artificial Intelligence (Tbilisi, Georgia,
		  USSR, Sept.\,3--8, 1975)},
  year = 1975,
  pages = {240--245},
  documenturl = {http://dli.iiit.ac.in/ijcai/IJCAI-75-VOL-1&2/PDF/034.pdf},
  keywords = {ifp; induction; inductive programming; lisp; pre-summers;
		  program synthesis}
}
 
K. Hausmann. Iterative and recursive modes of thinking in mathematical problem solving processes. In L. Streefland, editor, PME'95: Proceedings of the 9th International Conference for the Psychology of Mathematical Education (Noordwijkerhout, The Netherlands, 1985), pages 18-23. State University, 1985.
@inproceedings{hausmann:1985,
  author = {K. Hausmann},
  title = {Iterative and recursive modes of thinking in mathematical
		  problem solving processes},
  editor = {L. Streefland},
  booktitle = {{PME'95}: Proceedings of the 9th International Conference
		  for the Psychology of Mathematical Education
		  (Noordwijkerhout, The Netherlands, 1985)},
  year = 1985,
  pages = {18--23},
  publisher = {State University}
}
 
K. Haussmann and M. Reiss. Logo beginners problems with goal merging. In J. Hillel, editor, LME3: Proceedings of the 3rd International Conference for LOGO and Mathematics Education, pages 156-163. Concordia University, Montreal, Canada, 1987.
@incollection{haussmann/reiss:1987,
  author = {K. Haussmann and M. Reiss},
  title = {LOGO beginners problems with goal merging},
  editor = {J. Hillel},
  booktitle = {{LME3}: Proceedings of the 3rd International Conference
		  for LOGO and Mathematics Education},
  publisher = {Concordia University},
  year = 1987,
  pages = {156--163},
  address = {Montreal, Canada}
}
 
Robert Henderson. Incremental learning in inductive programming. In Ute Schmid, Emanuel Kitzelmann, and Rinus Plasmeijer, editors, Approaches and Applications of Inductive Programming. 3rd International Workshop, AAIP'09, Edinburgh, UK, Sept.4, 2009. Revised Papers, volume 5812 of Lecture Notes in Computer Science, pages 74-92, Berlin/Heidelberg, 2010. Springer.
@inproceedings{henderson:2010,
  author = {Robert Henderson},
  title = {Incremental Learning in Inductive Programming},
  editor = {Ute Schmid and Emanuel Kitzelmann and Rinus Plasmeijer},
  booktitle = {Approaches and Applications of Inductive Programming. 3rd
		  International Workshop, {AAIP'09}, Edinburgh, UK, Sept.\,4,
		  2009. Revised Papers},
  year = 2010,
  series = {Lecture Notes in Computer Science},
  volume = 5812,
  pages = {74--92},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-11930-9},
  url = {http://www.springerlink.com/content/1h137673x2428841/},
  abstract = {Inductive programming systems characteristically exhibit
		  an exponential explosion in search time as one increases
		  the size of the programs to be generated. As a way of
		  overcoming this, we introduce},
  keywords = {Inductive programming; inductive functional programming;
		  incremental learning},
  doi = {10.1007/978-3-642-11931-6_4},
  documenturl = {http://www.springerlink.com/content/1h137673x2428841/fulltext.pdf}
}
 
José Hernández-Orallo and M. JoséRamírez-Quintana. Inverse narrowing for the induction of functional logic programs. In José Luis Freire-Nistal, Moreno Falaschi, and Manuel Vilares Ferro, editors, APPIA-GULP-PRODE'98: Joint Conference on Declarative Programming (A Coruña, Spain, July20-23, 1998), pages 379-392, 1998.
@inproceedings{hernandez-orallo/joseramirez-quintana:1998,
  author = {Jos{\'e} Hern{\'a}ndez-Orallo and M.
		  Jos{\'e}Ram{\'i}rez-Quintana},
  title = {Inverse Narrowing for the Induction of Functional Logic
		  Programs},
  editor = {Jos{\'e} Luis Freire-Nistal and Moreno Falaschi and Manuel
		  Vilares Ferro},
  booktitle = {{APPIA-GULP-PRODE'98}: Joint Conference on Declarative
		  Programming (A Coru{\~n}a, Spain, July\,20--23, 1998)},
  year = 1998,
  pages = {379--392},
  keywords = {flip; iflp; inductive programming; ip-system; program
		  synthesis; recursion}
}
 
José Hernández-Orallo and M. JoséRamírez-Quintana. A Strong Complete Schema for Inductive Functional Logic Programming. In Saso Dzeroski and Peter A. Flach, editors, Inductive Logic Programming. 9th International Workshop, ILP'99, Bled, Slovenia, June24-27, 1999. Proceedings, volume 1634 of Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, pages 116-127, Berlin/Heidelberg, 1999. Springer.
@inproceedings{hernandez-orallo/joseramirez-quintana:1999,
  author = {Jos{\'e} Hern{\'a}ndez-Orallo and M.
		  Jos{\'e}Ram{\'i}rez-Quintana},
  title = {{A} {S}trong {C}omplete {S}chema for {I}nductive
		  {F}unctional {L}ogic {P}rogramming.},
  editor = {Saso Dzeroski and Peter A. Flach},
  booktitle = {Inductive Logic Programming. 9th International Workshop,
		  {ILP'99}, Bled, Slovenia, June\,24--27, 1999. Proceedings},
  year = 1999,
  series = {Lecture Notes in Computer Science. Lecture Notes in
		  Artificial Intelligence},
  volume = 1634,
  pages = {116--127},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  keywords = {flip; ifp; inductive programming; ip-system; program
		  synthesis; recursion},
  annote = {A Strong Complete Schmema for Inductive Functional Logic
		  Programming},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-66109-2},
  url = {http://www.springerlink.com/content/qxg8na20hwvug2nd/},
  abstract = {A new IFLP schema is presented as a general framework for
		  the induction of functional logic programs (FLP). Since
		  narrowing (which is the most usual operational semantics of
		  (FLP) performs a infication (mgu) followed by a
		  replacement, we introduce two main operators in our IFLP
		  schema: a generalisation and an inverse replacement or
		  property of equality. We prove that this schema
		  isstrongcomplete in tha way that, given some evidence, it
		  is possible to induce any program which could have
		  generated that evidence. We outline some possible
		  restrictions in order to improve the tractability of the
		  schema. We also show that inverse narrowing is just a
		  special case of our IFLP schema. Finally, a straightforward
		  extension of the IFLP schema to function invention is
		  illustrated.},
  doi = {10.1007/3-540-48751-4_12}
}
 
Thomas Hieber and Martin Hofmann. Automated Method Induction: Functional Goes Object Oriented. In Ute Schmid, Emanuel Kitzelmann, and Rinus Plasmeijer, editors, Approaches and Applications of Inductive Programming. 3rd International Workshop, AAIP'09, Edinburgh, UK, Sept.4, 2009. Revised Papers, volume 5812 of Lecture Notes in Computer Science, pages 159-173, Berlin/Heidelberg, 2010. Springer.
@inproceedings{hieber/hofmann:2010,
  author = {Thomas Hieber and Martin Hofmann},
  title = {{Automated Method Induction: Functional Goes Object
		  Oriented}},
  editor = {Ute Schmid and Emanuel Kitzelmann and Rinus Plasmeijer},
  booktitle = {Approaches and Applications of Inductive Programming. 3rd
		  International Workshop, {AAIP'09}, Edinburgh, UK, Sept.\,4,
		  2009. Revised Papers},
  year = 2010,
  series = {Lecture Notes in Computer Science},
  volume = 5812,
  pages = {159--173},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-11930-9},
  url = {http://www.springerlink.com/content/64563727294h5052/},
  abstract = {The development of software engineering has had a great
		  deal of benefits for the development of software. Along
		  with it came a whole new paradigm of the way software is
		  designed and implemented - object orientation. Today it is
		  a standard to have UML diagrams translated into program
		  code wherever possible. However, as few tools really go
		  beyond this we demonstrate a simple functional
		  representation for objects, methods and object-properties.
		  In addition we show how our inductive programming system},
  doi = {10.1007/978-3-642-11931-6_8},
  documenturl = {http://www.springerlink.com/content/64563727294h5052/fulltext.pdf}
}
 
Thomas Hieber, Martin Hofmann, Emanuel Kitzelmann, and Ute Schmid. Programming recursive functions by examples. In B. Velichkovsky Leon Urbas, T. Goschke, editor, Tagungsbericht der 9. Jahrestagung der Gesellschaft für Kognitionswissenschaft (KogWis 2008, TU Dresden, 28.9.-1.10. 2008), 2008. Nominiert für den Brain Products Poster Preis im Rahmender KogWis'08 (Platz 2).
@inproceedings{hieber_ea:2008,
  author = {Thomas Hieber and Martin Hofmann and Emanuel Kitzelmann
		  and Ute Schmid},
  title = {Programming Recursive Functions By Examples},
  editor = {Leon Urbas, T. Goschke, B. Velichkovsky},
  booktitle = {Tagungsbericht der 9. Jahrestagung der Gesellschaft
		  f{\"u}r Kognitionswissenschaft ({KogWis} 2008, TU Dresden,
		  28.9.--1.10. 2008)},
  year = 2008,
  note = {Nominiert f{\"u}r den Brain Products Poster Preis im
		  Rahmender {KogWis'08} (Platz 2)},
  isbn = {978-3-939025-14-6}
}
 
Martin Hofmann and Emanuel Kitzelmann. Input/Output guided detection of list catamorphisms: Towards problem specific use of program templates in IP. In John Gallagher and Janis Voigtländer, editors, Proceedings of the ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation (PEPM'10, Madrid, Jan.18-19, 2010), pages 93-100, New York, NY, USA, 2010. ACM Press. Co-located with POPL'10 (37th ACM SIGACT-SIGPLAN Symposium on Principles of Programming Languages (Madrid, Spain, Jan.18-22, 2010).
@inproceedings{hofmann/kitzelmann:2010,
  author = {Hofmann, Martin and Kitzelmann, Emanuel},
  title = {{Input/Output} Guided Detection of List Catamorphisms:
		  Towards Problem Specific Use of Program Templates in {IP}},
  editor = {John Gallagher and Janis Voigtl\"{a}nder},
  booktitle = {Proceedings of the {ACM} {SIGPLAN} Workshop on Partial
		  Evaluation and Program Manipulation ({PEPM'10}, Madrid,
		  Jan.\,18--19, 2010)},
  year = 2010,
  pages = {93--100},
  address = {New York, NY, USA},
  publisher = {{ACM} Press},
  note = {Co-located with {POPL'10} (37th {ACM} {SIGACT-SIGPLAN}
		  Symposium on Principles of Programming Languages (Madrid,
		  Spain, Jan.\,18--22, 2010)},
  isbn = {978-1-60558-727-1},
  keywords = {higher-order functions; inductive programming}
}
 
Martin Hofmann. Automatic Construction of XSL Templates - An Inductive Programming Approach. VDM Verlag, Saarbrücken, 2007.
@book{hofmann:2007,
  author = {Martin Hofmann},
  title = {{Automatic Construction of {XSL} Templates --- An
		  Inductive Programming Approach}},
  publisher = {VDM Verlag},
  year = 2007,
  address = {Saarbr\"{u}cken},
  school = {University of Bamberg},
  isbn = {978-3-639-00194-5},
  pages = 124,
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/theses/hofmann/hofmann.pdf}
}
 
Martin Hofmann. Igor2 - an analytical inductive functional programming system: Tool demo. In John Gallagher and Janis Voigtländer, editors, Proceedings of the ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation (PEPM'10, Madrid, Jan.18-19, 2010), pages 29-32, New York, NY, USA, 2010. ACM Press. Co-located with POPL'10 (37th ACM SIGACT-SIGPLAN Symposium on Principles of Programming Languages (Madrid, Spain, Jan.18-22, 2010).
@inproceedings{hofmann:2010,
  author = {Hofmann, Martin},
  title = {Igor2 -- An Analytical Inductive Functional Programming
		  System: Tool Demo},
  editor = {John Gallagher and Janis Voigtl{\"a}nder},
  booktitle = {Proceedings of the {ACM} {SIGPLAN} Workshop on Partial
		  Evaluation and Program Manipulation ({PEPM'10}, Madrid,
		  Jan.\,18--19, 2010)},
  year = 2010,
  pages = {29--32},
  address = {New York, NY, USA},
  publisher = {{ACM} Press},
  note = {Co-located with {POPL'10} (37th {ACM} {SIGACT-SIGPLAN}
		  Symposium on Principles of Programming Languages (Madrid,
		  Spain, Jan.\,18--22, 2010)},
  isbn = {978-1-60558-727-1},
  url = {http://doi.acm.org/10.1145/1706356.1706364}
}
 
Martin Hofmann. Data-driven detection of catamorphisms - towards prolem specific use of program schemes for inductive program synthesis. In TFP'10: Proceedings of the 11th Symposium on Trends in Functional Programming, (University of Oklahoma, Oklahoma City, USA, May17-19, 2010, 2010.
@inproceedings{hofmann:2010b,
  author = {Hofmann, Martin},
  title = {Data-Driven Detection of Catamorphisms --- Towards Prolem
		  Specific Use of Program Schemes for Inductive Program
		  Synthesis},
  booktitle = {{TFP'10}: Proceedings of the 11th Symposium on Trends in
		  Functional Programming, (University of Oklahoma, Oklahoma
		  City, USA, May\,17--19, 2010},
  year = 2010
}
 
Martin Hofmann, Andreas Hirschberger, Emanuel Kitzelmannn, and Ute Schmid. Inductive Synthesis of Recursive Functional Programs - A Comparison of Three Systems. In J. Hertzberg, M. Beetz, and R. Englert, editors, KI'07: Advances in Artificial Intelligence. 30th Annual German Conference on AI, KI'07, Osnabrück, Germany, Sept.10-13, 2007. Proceedings, volume 4667 of Lecture Notes in Computer Science, pages 468-472, Berlin/Heidelberg, 2007. Springer.
@inproceedings{hofmann_ea:2007,
  author = {Martin Hofmann and Andreas Hirschberger and Emanuel
		  Kitzelmannn and Ute Schmid},
  title = {{Inductive Synthesis of Recursive Functional Programs -- A
		  Comparison of Three Systems}},
  editor = {Hertzberg, J. and Beetz, M. and Englert, R.},
  booktitle = {KI'07: Advances in Artificial Intelligence. 30th Annual
		  German Conference on AI, {KI'07}, Osnabr\"uck, Germany,
		  Sept.\,10--13, 2007. Proceedings },
  year = 2007,
  series = {Lecture Notes in Computer Science},
  volume = 4667,
  pages = {468--472},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  abstract = {One of the most challenging subfields, and a still little
		  researched niche of machine learning, is the inductive
		  synthesis of recursive programs from incomplete
		  specifications, such as examples for the desired
		  input/output behavior.},
  keywords = {2007; adate; atre; automatic programming; dialogs;
		  functional programming; ilp; induction; inductive;
		  inductive functional programming; inductive inference;
		  inductive learning; inductive logic programming; inductive
		  program synthesis; inductive programming; inproceedings;
		  programming; published; },
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-74564-8},
  url = {http://www.springerlink.com/content/p03v78658627t0l2/},
  doi = {10.1007/978-3-540-74565-5_42}
}
 
Martin Hofmann, Emanuel Kitzelmann, and Ute Schmid. Analysis and evaluation of inductive programming systems in a higher-order framework. In A. Dengel, K. Berns, T. M. Breuel, F. Bomarius, and T. R. Roth-Berghofer, editors, KI'08: Advances in Artificial Intelligence. 31st Annual German Conference on AI, KI'08, Kaiserslautern, Germany, Sept.23-26, 2008. Proceedings, volume 5243 of Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, pages 78-86, Berlin/Heidelberg, 2008. Springer.
@inproceedings{hofmann_ea:2008,
  author = {Martin Hofmann and Emanuel Kitzelmann and Ute Schmid},
  title = {Analysis and Evaluation of Inductive Programming Systems
		  in a Higher-Order Framework},
  editor = {A. Dengel and K. Berns and T. M. Breuel and F. Bomarius
		  and T. R. Roth-Berghofer},
  booktitle = {{KI'08}: Advances in Artificial Intelligence. 31st Annual
		  German Conference on AI, {KI'08}, Kaiserslautern, Germany,
		  Sept.\,23--26, 2008. Proceedings},
  year = 2008,
  series = {Lecture Notes in Computer Science. Lecture Notes in
		  Artificial Intelligence},
  volume = 5243,
  pages = {78--86},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-85844-7},
  url = {http://www.springerlink.com/content/8348l7120633677l/},
  abstract = {In this paper we present a comparison of several inductive
		  programming (IP) systems. IP addresses the problem of
		  learning (recursive) programs from incomplete
		  specifications, such as input/output examples. First, we
		  introduce conditional higher-order term rewriting as a
		  common framework for inductive program synthesis. Then we
		  characterise the ILP systemGolemand the inductive
		  functional systemMagicHaskellerwithin this framework. In
		  consequence, we propose the inductive functional
		  systemIgorII as a powerful and efficient approach to IP.
		  Performance of all systems on a representative set of
		  sample problems is evaluated and shows the strength
		  ofIgorII.},
  doi = {10.1007/978-3-540-85845-4_10},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/publications/KI2008_submission_49(4).pdf},
  keywords = {analytical ip; enumerative ip; experiment; haskell;
		  higher-order functions; iflp; ifp; igor2; ilp; induction;
		  inductive programming; inproceedings; machine learning;
		  overview; program synthesis}
}
 
Martin Hofmann, Emanuel Kitzelmann, and Ute Schmid. A unifying framework for analysis and evaluation of inductive programming systems. In B. Goertzel, P. Hitzler, and M. Hutter, editors, Artificial General Intelligence. AGI'09: Proceedings of the 2nd Conference on Artificial General Intelligence (Arlington, Virginia, March6-9 2009), Advances in Intelligent Systems Research, pages 55-60. Atlantis Press, 2009.
@inproceedings{hofmann_ea:2009,
  author = {Martin Hofmann and Emanuel Kitzelmann and Ute Schmid},
  title = {A Unifying Framework for Analysis and Evaluation of
		  Inductive Programming Systems},
  editor = {B. Goertzel and P. Hitzler and M. Hutter},
  booktitle = {Artificial General Intelligence. {AGI'09}: Proceedings of
		  the 2nd Conference on Artificial General Intelligence
		  (Arlington, Virginia, March\,6--9 2009)},
  year = 2009,
  series = {Advances in Intelligent Systems Research},
  pages = {55--60},
  publisher = {Atlantis Press},
  isbn = {978-90-78677-24-6},
  url = {http://dx.doi.org/10.2991/agi.2009.16},
  keywords = {inductive programming},
  abstract = {In this paper we present a comparison of several inductive
		  programming (IP) systems. IP addresses the problem of
		  learning (recursive) programs from incomplete
		  specifications, such as input/output examples. First, we
		  introduce conditional higher-order term rewriting as a
		  common framework for inductive logic and inductive
		  functional program synthesis. Then we characterise the
		  several ILP systems which belong either to the most
		  recently researched or currently to the most powerful IP
		  systems within this framework. In consequence, we propose
		  the inductive functional system Igor2 as a powerful and
		  efficient approach to IP. Performance of all systems on a
		  representative set of sample problems is evaluated and
		  shows the strength of Igor2.}
}
 
Martin Hofmann, Emanuel Kitzelmann, and Ute Schmid. Porting Igor2 from Maude to Haskell. In Ute Schmid, Emanuel Kitzelmann, and Rinus Plasmeijer, editors, Approaches and Applications of Inductive Programming. 3rd International Workshop, AAIP'09, Edinburgh, UK, Sept.4, 2009. Revised Papers, volume 5812 of Lecture Notes in Computer Science, pages 140-158, Berlin/Heidelberg, 2010. Springer.
@inproceedings{hofmann_ea:2010,
  author = {Martin Hofmann and Emanuel Kitzelmann and Ute Schmid},
  title = {{Porting {Igor2} from {Maude} to {Haskell}}},
  editor = {Ute Schmid and Emanuel Kitzelmann and Rinus Plasmeijer},
  booktitle = {Approaches and Applications of Inductive Programming. 3rd
		  International Workshop, {AAIP'09}, Edinburgh, UK, Sept.\,4,
		  2009. Revised Papers},
  year = 2010,
  series = {Lecture Notes in Computer Science},
  volume = 5812,
  pages = {140--158},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-11930-9},
  url = {http://www.springerlink.com/content/w70304785057224g/},
  abstract = {This paper describes our efforts and solutions in porting
		  our IP system},
  doi = {10.1007/978-3-642-11931-6_7},
  documenturl = {http://www.springerlink.com/content/w70304785057224g/fulltext.pdf}
}
 
Chad Hogg and Hector Munoz-Avila. Learning hierarchical task networks from plan traces. In Proceedings of the Workshop on Artificial Intelligence Planning and Learning (Providence, Rhode Island, USA, Sept.22, 2007), 2007. In conjunction with the International Conference on Automated Planning and Scheduling (ICAPS'07).
@inproceedings{hogg/munoz-avila:2007,
  author = {Chad Hogg and Hector Munoz-Avila},
  title = {Learning Hierarchical Task Networks from Plan Traces},
  booktitle = {Proceedings of the Workshop on Artificial Intelligence
		  Planning and Learning (Providence, Rhode Island, USA,
		  Sept.\,22, 2007)},
  year = 2007,
  note = {In conjunction with the International Conference on
		  Automated Planning and Scheduling ({ICAPS'07})},
  url = {http://www.cs.umd.edu/~ukuter/icaps07aipl/},
  keywords = {learning-and-planning read}
}
 
Marcus Hutter, Eric Baum, and Emanuel Kitzelmann, editors. Artificial General Intelligence. AGI'10: Proceedings of the 3rd Conference on Artificial General Intelligence (Lugano, Switzerland, March5-8, 2010), Advances in Intelligent Systems Research. Atlantis Press, 2010.
@proceedings{hutter_ea:2010,
  title = {Artificial General Intelligence. {AGI'10}: Proceedings of
		  the 3rd Conference on Artificial General Intelligence
		  (Lugano, Switzerland, March\,5--8, 2010)},
  year = 2010,
  editor = {Marcus Hutter and Eric Baum and Emanuel Kitzelmann},
  series = {Advances in Intelligent Systems Research},
  publisher = {Atlantis Press},
  isbn = {978-90-78677-36-9},
  keywords = {AGI; AI}
}
 
Graham Hutton. A tutorial on the universality and expressiveness of fold. Journal of Functional Programming, 9:355-372, 1993.
@article{hutton:1993,
  author = {Graham Hutton},
  title = {A Tutorial on the Universality and Expressiveness of
		  Fold},
  journal = {{Journal of Functional Programming}},
  year = 1993,
  volume = 9,
  pages = {355--372},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.1618}
}
 
Peter Idestam-Almquist. Generalization under implication by recursive anti-unification. In P. Utgoff, editor, ICML'93: Proceedings of the 10th International Conference on Machine Learning (University of Massachusetts, Amherst, MA, USA, June27-29, 1993), pages 151-158. Morgan Kaufmann, 1993.
@inproceedings{idestam-almquist:1993,
  author = {Idestam-Almquist, Peter},
  title = {Generalization under Implication by Recursive
		  Anti-unification},
  editor = {P. Utgoff},
  booktitle = {{ICML'93}: Proceedings of the 10th International
		  Conference on Machine Learning (University of
		  Massachusetts, Amherst, MA, USA, June\,27--29, 1993)},
  year = 1993,
  pages = {151--158},
  publisher = {Morgan Kaufmann},
  isbn = {1-55860-307-7},
  annote = {ute-inflit}
}
 
Peter Idestam-Almquist. Recursive anti-unification. In Stephen H. Muggleton, editor, ILP'93: Proceedings Third International Workshop on Inductive Logic Programming (Ljubljana, Slovenia, 1993), pages 241-254. JSI, 1993.
@inproceedings{idestam-almquist:1993b,
  author = {Idestam-Almquist, Peter},
  title = {Recursive anti-unification},
  editor = {Stephen H. Muggleton},
  booktitle = {{ILP'93}: Proceedings Third International Workshop on
		  Inductive Logic Programming (Ljubljana, Slovenia, 1993)},
  year = 1993,
  pages = {241--254},
  publisher = {JSI}
}
 
Peter Idestam-Almquist. Efficient induction of recursive definitions by structural analysis of saturations. In Luc De Raedt, editor, ILP'95: Proceedings of the 5th International Workshop on Inductive Logic Programming (Tokyo, Japan, June17, 1995, 1995.
@inproceedings{idestam-almquist:1995,
  author = {Idestam-Almquist, Peter},
  title = {Efficient Induction of Recursive Definitions by Structural
		  Analysis of Saturations},
  editor = {De~Raedt, Luc},
  booktitle = {{ILP'95}: Proceedings of the 5th International Workshop on
		  Inductive Logic Programming (Tokyo, Japan, June\,17, 1995},
  year = 1995
}
 
Peter Idestam-Almquist. Efficient induction of recursive definitions by structural analysis of saturations. In Luc De Raedt, editor, Advances in Inductive Logic Programming. IOS Press, 1996.
@incollection{idestam-almquist:1996,
  author = {Idestam-Almquist, Peter},
  title = {Efficient Induction of Recursive Definitions by Structural
		  Analysis of Saturations},
  editor = {De~Raedt, Luc},
  booktitle = {Advances in Inductive Logic Programming},
  publisher = {IOS Press},
  year = 1996,
  keywords = {analytical ip; ilp; inductive programming; ip-system;
		  program synthesis; recursion; tim}
}
 
Nobuhiro Inuzuka, Masakage Kamo, Naohiro Ishii, Hirohisa Seki, and Hidenori Itoh. Top-down induction of logic programs from incomplete samples. In Inductive Logic Programming. 6th International Workshop, ILP-96 Stockholm, Sweden, Aug.26-28, 1996. Selected Papers, volume 1314 of Lecture Notes in Computer Science, pages 265-282, Berlin/Heidelberg, 1997. Springer.
@inproceedings{inuzuka_ea:1997,
  author = {Nobuhiro Inuzuka and Masakage Kamo and Naohiro Ishii and
		  Hirohisa Seki and Hidenori Itoh},
  title = {Top-down Induction of Logic Programs from Incomplete
		  Samples},
  booktitle = {Inductive Logic Programming. 6th International Workshop,
		  {ILP-96} Stockholm, Sweden, Aug.\,26--28, 1996. Selected
		  Papers},
  year = 1997,
  series = {Lecture Notes in Computer Science},
  volume = 1314,
  pages = {265--282},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  keywords = {FOIL-I; ilp; inductive programming; ip-system; program
		  synthesis; recursion},
  abstract = {We propose an ILP system FOIL-I, which induces logic
		  programs by a top-down method from incomplete samples. An
		  incomplete sample is constituted by some of positive
		  examples and negative examples on a finite domain. FOIL-I
		  has an evaluation function to estimate candidate
		  definitions, the function which is composition of an
		  information-based function and an encoding complexity
		  measure. FOILI uses a best-first search using the
		  evaluation function to make use of suspicious but necessary
		  candidates. Other particular points include a treatment for
		  recursive definitions and removal of redundant clauses.
		  Randomly selected incomplete samples are tested with
		  FOIL-I, QuinIan's FOIL and Muggleton's Progol. Compared
		  with others FOIL-I can induce target relations in many
		  cases from small incomplete samples.},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-63494-2},
  url = {http://www.springerlink.com/content/7811x2g2695417x5/},
  doi = {10.1007/3-540-63494-0_60}
}
 
A. Ishino and A. Yamamoto. Learning from examples with typed equational programming. In Algorithmic Learning Theory. 4th International Workshop on Analogical and Inductive Inference (AII'94, 5th International Workshop on Algorithmic Learning Theory (ALT'94), Reinhardsbrunn Castle, Germany Oct.10-15, 1994. Proceedings, volume 872 of Lecture Notes in Computer Science, pages 301-316. Springer, Berlin/Heidelberg, 1994.
@incollection{ishino/yamamoto:1994,
  author = {A. Ishino and A. Yamamoto},
  title = {Learning from examples with typed equational programming},
  booktitle = {Algorithmic Learning Theory. 4th International Workshop on
		  Analogical and Inductive Inference ({AII'94}, 5th
		  International Workshop on Algorithmic Learning Theory
		  ({ALT'94}), Reinhardsbrunn Castle, Germany Oct.\,10--15,
		  1994. Proceedings},
  publisher = {Springer},
  year = 1994,
  volume = 872,
  series = {Lecture Notes in Computer Science},
  pages = {301--316},
  address = {Berlin\,/\,Heidelberg},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-58520-6},
  url = {http://www.springerlink.com/content/f01326533257lh32/},
  abstract = {In this paper we present a constructive method of learning
		  from examples using typed equational programming. The main
		  contribution is a concept of type maintenance which appears
		  to be theoretically and practically useful. Type
		  maintenance is based on polymorphic types and is not
		  applicable to a type system without polymorphism. Because
		  equational programming possesses good properties of both
		  functional programming and logic programming, we will
		  refine results in inductive inference of logic programs and
		  that of functions. Our learning method is based on the type
		  maintenance, the generalization given by Plotkin and
		  Arimura et al. and the technique finding recursion given by
		  Summers.},
  doi = {10.1007/3-540-58520-6_73}
}
 
Alon Itai and Michael Slavkin. Detecting data structures from traces. In Emanuel Kitzelmann and Ute Schmid, editors, AAIP'07: Proceedings of the 2nd Workshop on Approaches and Applications of Inductive Programming (Warsaw, Poland, September17, 2007), pages 39-50, 2007. Work in Progress Report.
@inproceedings{itai/slavkin:2007,
  author = {Alon Itai and Michael Slavkin},
  title = {Detecting Data Structures from Traces},
  editor = {Emanuel Kitzelmann and Ute Schmid},
  booktitle = {{AAIP'07}: Proceedings of the 2nd Workshop on Approaches
		  and Applications of Inductive Programming (Warsaw, Poland,
		  September\,17, 2007)},
  year = 2007,
  pages = {39--50},
  note = {Work in Progress Report},
  url = {http://www.cogsys.wiai.uni-bamberg.de/aaip07/},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip07/aaip_print.pdf}
}
 
J.-J. Jeng and B. H. C. Cheng. Using analogy and formal methods for software reuse. In ICTAI'93: Proceedings of the Fifth International Conference on Tools with Artificial Intelligence (Boston, Massachusetts, USA, Nov.l8-11, 1993), pages 113-117, Los Alamitos, CA, USA, November 1993. IEEE Computer Society Press.
@inproceedings{jeng/cheng:1993,
  author = {J.-J. Jeng and B. H. C. Cheng},
  title = {Using Analogy and Formal Methods for Software Reuse},
  booktitle = {{ICTAI'93}: Proceedings of the Fifth International
		  Conference on Tools with Artificial Intelligence (Boston,
		  Massachusetts, USA, Nov.\,l8--11, 1993)},
  year = 1993,
  pages = {113--117},
  address = {Los Alamitos, CA, USA},
  month = {November},
  publisher = {IEEE Computer Society Press},
  isbn = {0-8186-4200-9}
}
 
Johan Jeuring, Alexey Rodriguez, and Gideon Smeding. Generating generic functions. In Ralf Hinze, editor, WGP'06: Proceedings of the ACM SIGPLAN Workshop on Generic Programming (Portland, Oregon, USA, Sept.16, 2006), pages 23-32, New York, NY, USA, 2006. ACM. Featured by ICFP'06: 11th ACM SIGPLAN International Conference on Functional Programming (Portland, Oregon, Sept.18-20, 2006).
@inproceedings{jeuring_ea:2006,
  author = {Johan Jeuring and Alexey Rodriguez and Gideon Smeding},
  title = {Generating generic functions},
  editor = {Ralf Hinze},
  booktitle = {{WGP'06}: Proceedings of the {ACM} {SIGPLAN} Workshop on
		  Generic Programming (Portland, Oregon, USA, Sept.\,16,
		  2006)},
  year = 2006,
  pages = {23--32},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  note = {Featured by {ICFP'06}: 11th {ACM} {SIGPLAN} International
		  Conference on Functional Programming (Portland, Oregon,
		  Sept.\,18--20, 2006)},
  url = {http://doi.acm.org/10.1145/1159861.1159865},
  isbn = {1-59593-492-6},
  keywords = {automated testing; enumerative ip; generic programming;
		  higher-order functions; ifp; induction; inductive
		  programming; inproceedings; program synthesis},
  abstract = {We present an approach to the generation of generic
		  functions from user-provided specifications. The
		  specifications consist of the type of a generic function,
		  examples of instances that it should "match" when
		  specialized, and properties that the generic function
		  should satisfy. We use the type-based function generator
		  Djinn to generate terms for specializations of the generic
		  function types on the type indices of generic functions.
		  Then we use QuickCheck to prune the generated terms by
		  testing against properties, and by testing specialized
		  candidate functions against the provided examples. Using
		  this approach we have been able to generate generic
		  equality, map, and zip functions, for example.}
}
 
Alípio Jorge and Pavel Brazdil. Architecture for iterative learning of recursive definitions. In Luc De Raedt, editor, Advances in Inductive Logic Programming. IOS Press, 1996.
@incollection{jorge/brazdil:1996,
  author = {Al\'{i}pio Jorge and Pavel Brazdil},
  title = {Architecture for Iterative Learning of Recursive
		  Definitions},
  editor = {De~Raedt, Luc},
  booktitle = {Advances in Inductive Logic Programming},
  publisher = {IOS Press},
  year = 1996,
  keywords = {SKILit; ilp; inductive programming; ip-system; program
		  synthesis; recursion},
  annote = {main technique: iterative bootstrap induction}
}
 
Alípio M. G. Jorge. Iterative Induction of Logic Programs. PhD thesis, Departamento de Ciência de Computadores, Universidade do Porto, 1998.
@phdthesis{jorge:1998,
  author = {Al\'{i}pio M. G. Jorge},
  title = {Iterative Induction of Logic Programs},
  school = {Departamento de Ci\^{e}ncia de Computadores, Universidade
		  do Porto},
  year = 1998,
  url = {http://www.liaad.up.pt/~amjorge/PhDThesis/},
  keywords = {SKILit; ilp; inductive programming; ip-system; program
		  synthesis; recursion}
}
 
J. P. Jouannaud and Yves Kodratoff. Characterization of a class of functions synthesized from examples by a Summers like method using a `B.M.W.'matching technique. In IJCAI'79: Proceedings of the 6th International Joint Conference on Artificial Intelligence (Tokyo, Japan, Aug.20-23, 1979), pages 440-447. Morgan Kaufmann, 1979.
@inproceedings{jouannaud/kodratoff:1979,
  author = {J. P. Jouannaud and Yves Kodratoff},
  title = {Characterization of a Class of Functions Synthesized from
		  Examples by a {Summers} like method using a
		  {`B.M.W.'}Matching Technique},
  booktitle = {{IJCAI}'79: Proceedings of the 6th International Joint
		  Conference on Artificial Intelligence (Tokyo, Japan,
		  Aug.\,20--23, 1979)},
  year = 1979,
  pages = {440--447},
  publisher = {Morgan Kaufmann},
  keywords = {analytical ip; ifp; induction; inductive programming;
		  program synthesis; synthesis from traces}
}
 
Jean-Pierre Jouannaud and Yves Kodratoff. Program synthesis from examples of behavior. In Alan W. Biermann and Gérard Guiho, editors, Computer Program Synthesis Methodologies, pages 213-250. D. Reidel Publ. Co., 1983.
@incollection{jouannaud/kodratoff:1983,
  author = {Jean-Pierre Jouannaud and Yves Kodratoff},
  title = {Program Synthesis from Examples of Behavior},
  editor = {Alan W. Biermann and G\'{e}rard Guiho},
  booktitle = {Computer Program Synthesis Methodologies},
  publisher = {D. Reidel Publ. Co.},
  year = 1983,
  pages = {213--250},
  keywords = {analytical ip; ifp; induction; inductive programming;
		  program synthesis}
}
 
Stefan Kahrs. Genetic programming with primitive recursion. In GECCO'06: Proceedings of the 8th Proceedings of the 8th annual Conference on Genetic and Evolutionary Computation (Seattle, Washington, USA, July08-12, 2006), pages 941-942, New York, NY, USA, 2006. ACM. Poster Session “Genetic programming: posters”.
@inproceedings{kahrs:2006,
  author = {Stefan Kahrs},
  title = {Genetic Programming with Primitive Recursion},
  booktitle = {{GECCO'06}: Proceedings of the 8th Proceedings of the 8th
		  annual Conference on Genetic and Evolutionary Computation
		  (Seattle, Washington, USA, July\,08--12, 2006)},
  year = 2006,
  pages = {941--942},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  note = {Poster Session ``Genetic programming: posters''},
  url = {http://doi.acm.org/10.1145/1143997.1144160},
  keywords = {enumerative ip; gp; ifp; induction; inductive programming;
		  primitive recursion; program evolution; program synthesis},
  abstract = {When Genetic Programming is used to evolve arithmetic
		  functions it often operates by composing them from a fixed
		  collection of elementary operators and applying them to
		  parameters or certain primitive constants. This limits the
		  expressiveness of the programs that can be evolved. It is
		  possible to extend the expressiveness of such an approach
		  significantly without leaving the comfort of terminating
		  programs by including primitive recursion as a control
		  operation.The technique used here was gene expression
		  programming [2], a variation of grammatical evolution [8].
		  Grammatical evolution avoids the problem of program bloat;
		  its separation of genotype (string of symbols) and
		  phenotype (expression tree) permits to optimise the
		  generated programs without interfering with the
		  evolutionary process.}
}
 
Stefan Kahrs. The primitive recursive functions are recursively enumerable, 2008.
@misc{kahrs:2008,
  author = {Stefan Kahrs},
  title = {The Primitive Recursive Functions are Recursively
		  Enumerable},
  year = 2008,
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.62.9712},
  documenturl = {http://www.cs.kent.ac.uk/people/staff/smk/primrec.pdf},
  keywords = {computability; recursion theory},
  abstract = {Abstract. Meta-operations on primitive recursive functions
		  sit at the brink of what is computationally possible: the
		  semantic equality of primitive recursive programs is
		  undecidable, and yet this paper shows that the whole class
		  of p.r. functions can be enumerated without semantic
		  duplicates. More generally, the construction shows that for
		  any equivalence relation \approx on natural numbers, N/
		  \approx is r.e. if \approx is co-semi-decidable.}
}
 
Susumu Katayama. Power of brute-force search in strongly-typed inductive functional programming automation. In Chengqi Zhang, Hans W. Guesgen, and Wai-Kiang Yeap, editors, PRICAI'04: Trends in Artificial Intelligence. 8th Pacific Rim International Conference on Artificial Intelligence, Auckland, New Zealand, Aug.9-13, 2004. Proceedings, volume 3157 of Lecture Notes in Computer Science, pages 75-84, Berlin/Heidelberg, 2004. Springer.
@inproceedings{katayama:2004,
  author = {Susumu Katayama},
  title = {Power of Brute-Force Search in Strongly-Typed Inductive
		  Functional Programming Automation},
  editor = {Chengqi Zhang and Hans W. Guesgen and Wai-Kiang Yeap},
  booktitle = {{PRICAI'04}: Trends in Artificial Intelligence. 8th
		  Pacific Rim International Conference on Artificial
		  Intelligence, Auckland, New Zealand, Aug.\,9--13, 2004.
		  Proceedings},
  year = 2004,
  series = {Lecture Notes in Computer Science},
  volume = 3157,
  pages = {75--84},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-22817-2},
  url = {http://springerlink.metapress.com/content/u6gc1dt4yl9cmpkl/},
  doi = {10.1007/978-3-540-28633-2_10},
  abstract = {A successful case of applying brute-force search to
		  functional programming automation is presented and compared
		  with a conventional genetic programming method. From the
		  information of the type and the property that should be
		  satisfied, this algorithm is able to find automatically the
		  shortest Haskell program using the set of function
		  components (or library) configured beforehand, and there is
		  no need to design the library every time one requests a new
		  functional program. According to the presented experiments,
		  programs consisted of several function applications can be
		  found within some seconds even if we always use the library
		  designed for general use. In addition, the proposed
		  algorithm can efficiently tell the number of possible
		  functions of given size that are consistent with the given
		  type, and thus can be a tool to evaluate other methods like
		  genetic programming by providing the information of the
		  baseline performance.},
  keywords = {MagicHaskeller; PolyGP; comparison; enumerative ip;
		  higher-order functions; ifp; induction; inductive
		  programming; program synthesis; }
}
 
Susumu Katayama. Library for systematic search for expressions. In AIC'06: Proceedings of the 6th WSEAS International Conference on Applied Informatics and Communications (Elounda, Agios Nikolaos, Crete Island, Greece, Aug.18-20, 2006), pages 381-387, Stevens Point, Wisconsin, USA, 2006. World Scientific and Engineering Academy and Society (WSEAS).
@inproceedings{katayama:2006,
  author = {Katayama, Susumu},
  title = {Library for systematic search for expressions},
  booktitle = {{AIC'06}: Proceedings of the 6th {WSEAS} International
		  Conference on Applied Informatics and Communications
		  (Elounda, Agios Nikolaos, Crete Island, Greece,
		  Aug.\,18--20, 2006)},
  year = 2006,
  pages = {381--387},
  address = {Stevens Point, Wisconsin, USA},
  publisher = {World Scientific and Engineering Academy and Society
		  (WSEAS)},
  isbn = {960-8457-51-3}
}
 
Susumu Katayama. Systematic search for lambda expressions. In Marko C. J. D. van Eekelen, editor, TFP'05: Revised Selected Papers from the Sixth Symposium on Trends in Functional Programming (Tallinn, Estonia, Sep.23-24, 2005), volume 6 of Trends in Functional Programming, pages 111-126. Intellect Books, 2007.
@inproceedings{katayama:2007,
  author = {Susumu Katayama},
  title = {Systematic Search for Lambda Expressions},
  editor = {Marko C. J. D. van Eekelen},
  booktitle = {{TFP'05}: Revised Selected Papers from the Sixth Symposium
		  on Trends in Functional Programming (Tallinn, Estonia,
		  Sep.\,23--24, 2005)},
  year = 2007,
  series = {Trends in Functional Programming},
  volume = 6,
  pages = {111--126},
  publisher = {Intellect Books},
  isbn = {978-1-84150-176-5},
  documenturl = {http://www.cs.ioc.ee/tfp-icfp-gpce05/tfp-proc/14num.pdf},
  keywords = {MagicHaskeller; enumerative ip; higher-order functions;
		  ifp; induction; inductive programming; inproceedings;
		  program synthesis; recursion schemes}
}
 
Susumu Katayama. Efficient exhaustive generation of functional programs using monte-carlo search with iterative deepening. In PRICAI'08: Trends in Artificial Intelligence. 10th Pacific Rim International Conference on Artificial Intelligence, Hanoi, Vietnam, Dec.15-19, 2008. Proceedings, volume 5351 of Lecture Notes in Computer Science, pages 199-210, Berlin/Heidelberg, 2008. Springer.
@inproceedings{katayama:2008,
  author = {Susumu Katayama},
  title = {Efficient Exhaustive Generation of Functional Programs
		  Using Monte-Carlo Search with Iterative Deepening},
  booktitle = {{PRICAI'08}: Trends in Artificial Intelligence. 10th
		  Pacific Rim International Conference on Artificial
		  Intelligence, Hanoi, Vietnam, Dec.\,15--19, 2008.
		  Proceedings},
  year = 2008,
  series = {Lecture Notes in Computer Science},
  volume = 5351,
  pages = {199--210},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-89196-3},
  url = {http://www.springerlink.com/content/mh400717k763u162/},
  abstract = {Genetic programming and inductive synthesis of functional
		  programs are two major approaches to inductive functional
		  programming. Recently, in addition to them, some
		  researchers pursue efficient exhaustive program generation
		  algorithms, partly for the purpose of providing a
		  comparator and knowing how essential the ideas such as
		  heuristics adopted by those major approaches are, partly
		  expecting that approaches that exhaustively generate
		  programs with the given type and pick up those which
		  satisfy the given specification may do the task well. In
		  exhaustive program generation, since the number of programs
		  exponentially increases as the program size increases, the
		  key to success is how to restrain the exponential bloat by
		  suppressing semantically equivalent but syntactically
		  different programs. In this paper we propose an algorithm
		  applying random testing of program equivalences (or
		  Monte-Carlo search for functional differences) to the
		  search results of iterative deepening, by which we can
		  totally remove redundancies caused by semantically
		  equivalent programs. Our experimental results show that
		  applying our algorithm to subexpressions during program
		  generation remarkably reduces the computational costs when
		  applied to rich primitive sets.},
  doi = {10.1007/978-3-540-89197-0_21}
}
 
Susumu Katayama. Recent improvements of magichaskeller. In Ute Schmid, Emanuel Kitzelmann, and Rinus Plasmeijer, editors, Approaches and Applications of Inductive Programming. 3rd International Workshop, AAIP'09, Edinburgh, UK, Sept.4, 2009. Revised Papers, volume 5812 of Lecture Notes in Computer Science, pages 174-193, Berlin/Heidelberg, 2010. Springer.
@inproceedings{katayama:2010,
  author = {Susumu Katayama},
  title = {Recent Improvements of MagicHaskeller},
  editor = {Ute Schmid and Emanuel Kitzelmann and Rinus Plasmeijer},
  booktitle = {Approaches and Applications of Inductive Programming. 3rd
		  International Workshop, {AAIP'09}, Edinburgh, UK, Sept.\,4,
		  2009. Revised Papers},
  year = 2010,
  series = {Lecture Notes in Computer Science},
  volume = 5812,
  pages = {174--193},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-11930-9},
  url = {http://www.springerlink.com/content/ejx67u1835q7j757/},
  abstract = {MagicHaskeller is our inductive functional programming
		  library based on systematic search. In this paper we
		  introduce two recent improvements to MagicHaskeller, i.e.
		  1) clarification and extension to arbitrary-rank
		  polymorphism of its algorithm, and 2) efficiency
		  improvement in its filtration algorithm that removes
		  redundancy in the search results.},
  doi = {10.1007/978-3-642-11931-6_9},
  keywords = {inductive programming; magichaskeller},
  documenturl = {http://www.springerlink.com/content/ejx67u1835q7j757/fulltext.pdf}
}
 
Oleg Kiselyov and Ralf Lämmel. Haskell's overlooked object system, 2005.
@misc{kiselyov/laemmel:2005,
  author = {Oleg Kiselyov and Ralf L\"ammel},
  title = {Haskell's overlooked object system},
  year = 2005,
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0509027}
}
 
Oleg Kiselyov, Ralf Lämmel, and Keean Schupke. Strongly typed heterogeneous collections. In Haskell'04: Proceedings of the ACM SIGPLAN workshop on Haskell (Snowbird, Utah, USA, Sept.22-22, 2004), pages 96-107. ACM Press, 2004.
@inproceedings{kiselyov_ea:2004,
  author = {Oleg Kiselyov and Ralf L{\"a}mmel and Keean Schupke},
  title = {{Strongly typed heterogeneous collections}},
  booktitle = {{Haskell'04}: Proceedings of the {ACM} {SIGPLAN} workshop
		  on Haskell (Snowbird, Utah, USA, Sept.\,22--22, 2004)},
  year = 2004,
  pages = {96--107},
  publisher = {{ACM} Press},
  url = {http://doi.acm.org/10.1145/1017472.1017488},
  isbn = {1-58113-850-4}
}
 
Emanuel Kitzelmann and Martin Hofmann. IgorII: An inductive functional programming prototype. In Mailik Ghallab, Constantine D. Spyropoulos, Nikos Fakotakis, and Nikos Avouris, editors, ECAI'08: Proceedings of the System Demonstrations of the 18th European Conference on Artificial Intelligence (Patras, Greece, July21-25, 2008), volume 178 of Frontiers in Artificial Intelligence and Applications, pages 29-30, Amsterdam, Netherlands, 2008. IOS Press.
@inproceedings{kitzelmann/hofmann:2008,
  author = {Emanuel Kitzelmann and Martin Hofmann},
  title = {{IgorII}: An Inductive Functional Programming Prototype},
  editor = {Mailik Ghallab and Constantine D. Spyropoulos and Nikos
		  Fakotakis and Nikos Avouris},
  booktitle = {{ECAI'08}: Proceedings of the System Demonstrations of the
		  18th European Conference on Artificial Intelligence
		  (Patras, Greece, July\,21--25, 2008)},
  year = 2008,
  series = {Frontiers in Artificial Intelligence and Applications},
  volume = 178,
  pages = {29--30},
  address = {Amsterdam, Netherlands},
  publisher = {IOS Press},
  isbn = {978-960-6843-17-4}
}
 
Emanuel Kitzelmann and Ute Schmid. An explanation based generalization approach to inductive synthesis of functional programs. In Emanuel Kitzelmann, Roland J. Olsson, and Ute Schmid, editors, AAIP'05: Proceedings of the 1st Workshop on Approaches and Applications of Inductive Programming (Bonn, Germany, Aug.7, 2005), pages 15-26, 2005. Full Paper.
@inproceedings{kitzelmann/schmid:2005,
  author = {Emanuel Kitzelmann and Ute Schmid},
  title = {An Explanation Based Generalization Approach to Inductive
		  Synthesis of Functional Programs},
  editor = {Emanuel Kitzelmann and Roland J. Olsson and Ute Schmid},
  booktitle = {{AAIP'05}: Proceedings of the 1st Workshop on Approaches
		  and Applications of Inductive Programming (Bonn, Germany,
		  Aug.\,7, 2005)},
  year = 2005,
  pages = {15--26},
  note = {Full Paper},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip05/proceedings/aaip05_ifps.pdf},
  keywords = {analytical ip; ebg; ifp; igor1; induction; inductive
		  programming; inproceedings; machine learning; program
		  synthesis; recursive program schemes}
}
 
Emanuel Kitzelmann and Ute Schmid. Inductive synthesis of functional programs: An explanation based generalization approach. Journal of Machine Learning Research, 7(Feb):429-454, 2006.
@article{kitzelmann/schmid:2006,
  author = {Emanuel Kitzelmann and Ute Schmid},
  title = {Inductive Synthesis of Functional Programs: An Explanation
		  Based Generalization Approach},
  journal = {Journal of Machine Learning Research},
  year = 2006,
  volume = 7,
  number = {Feb},
  pages = {429--454},
  address = {Cambridge, MA, USA},
  publisher = {MIT Press},
  issn = {1533-7928},
  url = {http://jmlr.csail.mit.edu/papers/v7/kitzelmann06a.html},
  keywords = {analytical ip; article; ebg; ifp; igor1; induction;
		  inductive programming; program synthesis; recursive program
		  schemes},
  annote = {Inductive Synthesis of Functional Programs: An Explanation
		  Based Generalization Approach},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/publications/JMLR-05-164-1.pdf},
  abstract = {We describe an approach to the inductive synthesis of
		  recursive equations from input/output-examples which is
		  based on the classical two-step approach to induction of
		  functional Lisp programs of Summers (1977). In a first
		  step, I/O-examples are rewritten to traces which explain
		  the outputs given the respective inputs based on a datatype
		  theory. These traces can be integrated into one conditional
		  expression which represents a non-recursive program. In a
		  second step, this initial program term is generalized into
		  recursive equations by searching for syntactical
		  regularities in the term. Our approach extends the
		  classical work in several aspects. The most important
		  extensions are that we are able to induce a set of
		  recursive equations in one synthesizing step, the equations
		  may contain more than one recursive call, and additionally
		  needed parameters are automatically introduced.}
}
 
Emanuel Kitzelmann and Ute Schmid. Induction of functional programs based on relations between Input/Output examples. In C. Freksa, M. Kohlhase, and K. Schill, editors, KI'06: Proceedings of 29th Annual German Conference on Artificial Intelligence (Bremen, June14-19, 2006), 2006. Poster Abstract.
@inproceedings{kitzelmann/schmid:2006b,
  author = {Emanuel Kitzelmann and Ute Schmid},
  title = {Induction of Functional Programs based on Relations
		  between {Input/Output} Examples},
  editor = {C. Freksa and M. Kohlhase and K. Schill},
  booktitle = {{KI'06}: Proceedings of 29th Annual German Conference on
		  Artificial Intelligence (Bremen, June\,14--19, 2006)},
  year = 2006,
  note = {Poster Abstract},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/publications/ki06extabst.pdf},
  keywords = {2006; automatic programming; constructor systems; extended
		  abstract; functional programming; igor2; induction;
		  inductive; inductive functional programming; inductive
		  inference; inductive program synthesis; inductive
		  programming; myown; programming}
}
 
Emanuel Kitzelmann and Ute Schmid. Inducing constructor systems from example-terms by detecting syntactical regularities. In M. Fernández and R. Lämmel, editors, RULE'06: Proceedings of the 7th International Workshop on Rule Based Programming (Seattle, USA, Aug.11, 2006), volume 174 of Electronic Notes in Theoretical Computer Science, pages 49-63, Essex, UK, April 2007. Elsevier Science Publishers Ltd.
@inproceedings{kitzelmann/schmid:2007,
  author = {Emanuel Kitzelmann and Ute Schmid},
  title = {Inducing Constructor Systems from Example-Terms by
		  Detecting Syntactical Regularities},
  editor = {M. Fern{\'a}ndez and R. L{\"a}mmel},
  booktitle = {{RULE'06}: Proceedings of the 7th International Workshop
		  on Rule Based Programming (Seattle, USA, Aug.\,11, 2006)},
  year = 2007,
  series = {Electronic Notes in Theoretical Computer Science},
  volume = 174,
  pages = {49--63},
  address = {Essex, UK},
  month = {April},
  publisher = {Elsevier Science Publishers Ltd.},
  number = 1,
  url = {http://dx.doi.org/10.1016/j.entcs.2006.11.015},
  abstract = {We present a technique for inducing functional programs
		  from few, well chosen input/output-examples (I/O-examples).
		  Potential applications for automatic program or algorithm
		  induction are to enable end users to create their own
		  simple programs, to assist professional programmers, or to
		  automatically invent completely new and efficient
		  algorithms. In our approach, functional programs are
		  represented as constructor term rewriting systems (CSs)
		  containing recursive rules. I/O-examples for a target
		  function to be implemented are a set of pairs of terms
		  (F(i_i),o_i) meaning that F(i_i)---denoting application of
		  function F to input i_i---is rewritten to o_i by a CS
		  implementing the function F. Induction is based on
		  detecting syntactic regularities between example terms. In
		  this paper we present theoretical results and describe an
		  algorithm for inducing CSs over arbitrary signatures/data
		  types which consist of one function defined by an arbitrary
		  number of rules with an arbitrary number of non-nested
		  recursive calls in each rule. Moreover, we present
		  empirical results based on a prototypical implementation.},
  annote = {ScienceDirect - Electronic Notes in Theoretical Computer
		  Science : Inducing Constructor Systems from Example-Terms
		  by Detecting Syntactical Regularities},
  keywords = {2007; article; automatic programming; constructor systems;
		  functional programming; igor2; induction; inductive;
		  inductive inference; inductive program synthesis; inductive
		  programming; myown; programming; published; rule-based
		  programming},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/publications/rule06.pdf}
}
 
Emanuel Kitzelmann and Ute Schmid, editors. AAIP'07: Proceedings of the 2nd Workshop on Approaches and Applications of Inductive Programming (Warsaw, Poland, Sep.17, 2007, 2007. In conjunction with the 18th European Conference on Machine Learning (ECML).
@proceedings{kitzelmann/schmid:2007b,
  title = {{AAIP'07}: Proceedings of the 2nd Workshop on Approaches
		  and Applications of Inductive Programming (Warsaw, Poland,
		  Sep.\,17, 2007},
  year = 2007,
  editor = {Emanuel Kitzelmann and Ute Schmid},
  note = {In conjunction with the 18th European Conference on
		  Machine Learning ({ECML})},
  keywords = {2007; automatic programming; induction; inductive program
		  synthesis; inductive programming; machine learning;
		  proceedings; programming; },
  url = {http://www.cogsys.wiai.uni-bamberg.de/aaip07/},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip07/aaip_print.pdf},
  size = {59 pages}
}
 
Emanuel Kitzelmann. Grundlegende Ansätze zur Induktiven Synthese Funktionaler Programme (Summers und Biermann). Kitzelmann, 2001.
@unpublished{kitzelmann:2001,
  author = {Emanuel Kitzelmann},
  title = {{Grundlegende Ans{\"a}tze zur Induktiven Synthese
		  Funktionaler Programme (Summers und Biermann)}},
  year = 2001,
  note = {Kitzelmann},
  keywords = {2001; automatic programming; functional programming;
		  induction; inductive; inductive functional programming;
		  inductive inference; inductive program synthesis; inductive
		  programming; myown; programming; term paper},
  school = {{Technische Universit{\"a}t Berlin}}
}
 
Emanuel Kitzelmann. Inductive functional program synthesis - a term-construction and folding approach. Diplomarbeit, Technische Universität Berlin, 2003. Unpublished.
@mastersthesis{kitzelmann:2003,
  author = {Emanuel Kitzelmann},
  title = {Inductive Functional Program Synthesis -- A
		  Term-Construction and Folding Approach},
  school = {{Technische Universit{\"a}t Berlin}},
  year = 2003,
  type = {Diplomarbeit},
  note = {Unpublished},
  keywords = {analytical ip; ifp; igor1; induction; inductive
		  programming; mastersthesis; program synthesis; recursive
		  program schemes},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/kitzelmann/documents/thesis.ps}
}
 
Ute Kitzelmann, Emanuel an Schmid. An ebg approach to the inductive synthesis of functional programs. In Luc De Raedt and Stefan Wrobel, editors, ICML'05: Proceedings of the 22nd International Conference on Machine Learning (Bonn, Germany, Aug.7-11, 2005), volume 119 of ACM International Conference Proceeding Series, pages 15-26. ACM, 2005.
@inproceedings{kitzelmann:2005,
  author = {Kitzelmann, Emanuel an Schmid, Ute},
  title = {An EBG Approach to the Inductive Synthesis of Functional
		  Programs},
  editor = {De~Raedt, Luc and Wrobel, Stefan},
  booktitle = {{ICML'05}: Proceedings of the 22nd International
		  Conference on Machine Learning (Bonn, Germany, Aug.\,7--11,
		  2005)},
  year = 2005,
  series = {{ACM} International Conference Proceeding Series},
  volume = 119,
  pages = {15--26},
  publisher = {{ACM}},
  isbn = {1-59593-180-5}
}
 
Emanuel Kitzelmann. Data-driven induction of recursive functions from Inpuit/Output-examples. In Emanuel Kitzelmann and Ute Schmid, editors, AAIP'07: Proceedings of the 2nd Workshop on Approaches and Applications of Inductive Programming (Warsaw, Poland, September17, 2007), pages 15-26, 2007. Full Paper.
@inproceedings{kitzelmann:2007,
  author = {Emanuel Kitzelmann},
  title = {Data-Driven Induction of Recursive Functions from
		  {Inpuit/Output}-Examples},
  editor = {Emanuel Kitzelmann and Ute Schmid},
  booktitle = {{AAIP'07}: Proceedings of the 2nd Workshop on Approaches
		  and Applications of Inductive Programming (Warsaw, Poland,
		  September\,17, 2007)},
  year = 2007,
  pages = {15--26},
  note = {Full Paper},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/publications/aaip07.pdf},
  url = {http://www.cogsys.wiai.uni-bamberg.de/aaip07/},
  keywords = {2007; automatic programming; constructor systems;
		  functional programming; igor2; induction; inductive
		  functional programming; inductive program synthesis;
		  inductive programming; inproceedings; programming; }
}
 
Emanuel Kitzelmann. Data-driven learning of functions over algebraic datatypes from Input/Output-examples. In Peter Geibel and Brijnesh J. Jain, editors, LNVD'07: Learning from Non-Vectorial Data. Proceedings of the KI'07 Workshop, Osnabrück, Germany, Sept.10, 2007, volume 6 of Publications of the Institute of Cognitive Science, pages 36-45. Institute of Cognitive Science, Universität Osnabrück, 2007.
@inproceedings{kitzelmann:2007b,
  author = {Emanuel Kitzelmann},
  title = {Data-Driven Learning of Functions over Algebraic Datatypes
		  from {Input/Output}-Examples},
  editor = {Peter Geibel and Brijnesh J. Jain},
  booktitle = {{LNVD'07}: Learning from Non-Vectorial Data. Proceedings
		  of the {KI'07} Workshop, Osnabr\"uck, Germany, Sept.\,10,
		  2007},
  year = 2007,
  series = {Publications of the Institute of Cognitive Science},
  volume = 6,
  pages = {36--45},
  publisher = {Institute of Cognitive Science, {Universit{\"a}t
		  Osnabr{\"u}ck}},
  keywords = {igor2; inductive programming},
  abstract = {We describe a technique for inducing recursive functional
		  programs over algebraic datatypes from few non-recursive
		  and only positive ground example-equations. Induction is
		  data-driven and based onstructural regularities between
		  example terms. In our approach, functional programs are
		  represented as constructor term rewriting systems
		  containing recursive rewrite rules. In addition to the
		  examples for the target functions, background knowledge
		  functions that may be called by the induced functions can
		  be given in form of ground equations. Our algorithm induces
		  several dependent recursive target functions over arbitrary
		  user-defined algebraic datatypes in one step and
		  automatically introduces auxiliary subfunctions if needed.
		  We have implemented a prototype of the described method and
		  applied it to a number of problems.},
  documenturl = {http://www.cogsci.uni-osnabrueck.de/cogsci/dirs/dynamic/publications/PICSvol6_2007.pdf}
}
 
Emanuel Kitzelmann. Analytical inductive functional programming. In Michael Hanus, editor, LOPSTR'08: Pre-Proceedings of the 18th International Symposium on Logic-Based Program Synthesis and Transformation (Valencia, Spain, July17-18, 2008), pages 166-180, 2008.
@inproceedings{kitzelmann:2008,
  author = {Emanuel Kitzelmann},
  title = {Analytical Inductive Functional Programming},
  editor = {Michael Hanus},
  booktitle = {{LOPSTR'08}: Pre-Proceedings of the 18th International
		  Symposium on Logic-Based Program Synthesis and
		  Transformation (Valencia, Spain, July\,17--18, 2008)},
  year = 2008,
  pages = {166--180},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/publications/lopstr08pre.pdf}
}
 
Emanuel Kitzelmann. Data-driven induction of functional programs. In Malik Ghallab, Constantine D. Spyropoulos, Nikos Fakotakis, and Nikos Avouris, editors, ECAI'08: Proceedings of the 18th European Conference on Artificial Intelligence (Patras, Greece, July21-25, 2008), volume 178 of Frontiers in Artificial Intelligence and Applications, pages 781-782, Amsterdam, Netherlands, 2008. IOS Press.
@inproceedings{kitzelmann:2008b,
  author = {Emanuel Kitzelmann},
  title = {Data-Driven Induction of Functional Programs},
  editor = {Malik Ghallab and Constantine D. Spyropoulos and Nikos
		  Fakotakis and Nikos Avouris},
  booktitle = {{ECAI'08}: Proceedings of the 18th European Conference on
		  Artificial Intelligence (Patras, Greece, July\,21--25,
		  2008)},
  year = 2008,
  series = {Frontiers in Artificial Intelligence and Applications},
  volume = 178,
  pages = {781--782},
  address = {Amsterdam, Netherlands},
  publisher = {IOS Press},
  isbn = {978-960-6843-17-4},
  keywords = {analytical ip; constructor systems; extended abstract;
		  ifp; igor2; induction; inductive programming;
		  inproceedings; program synthesis},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/publications/ecai08.pdf}
}
 
Emanuel Kitzelmann. Analytical inductive functional programming. In Michael Hanus, editor, Logic-Based Program Synthesis and Transformation. 18th International Symposium, LOPSTR'08, Valencia, Spain, July17-18, 2008. Revised Selected Papers, volume 5438 of Lecture Notes in Computer Science, pages 87-102, Berlin/Heidelberg, 2009. Springer.
@inproceedings{kitzelmann:2009,
  author = {Emanuel Kitzelmann},
  title = {Analytical Inductive Functional Programming},
  editor = {Michael Hanus},
  booktitle = { Logic-Based Program Synthesis and Transformation. 18th
		  International Symposium, {LOPSTR'08}, Valencia, Spain,
		  July\,17--18, 2008. Revised Selected Papers},
  year = 2009,
  series = {Lecture Notes in Computer Science},
  volume = 5438,
  pages = {87--102},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-00514-5},
  url = {http://www.springerlink.com/content/9lv2308431533337/},
  abstract = {We describe a new method to induce functional programs
		  from small sets of non-recursive equations representing a
		  subset of their input-output behaviour. Classical attempts
		  to construct functionalLispprograms from
		  input/output-examples areanalytical, i.e., aLispprogram
		  belonging to a strongly restricted program class is
		  algorithmically derived from examples. More recent
		  approaches enumerate candidate programs and onlytestthem
		  against the examples until a program which correctly
		  computes the examples is found. Theoretically, large
		  program classes can be induced generate-and-test based, yet
		  this approach suffers from combinatorial explosion. We
		  propose a combination of search and analytical techniques.
		  The method described in this paper is search based in order
		  to avoid strong a-priori restrictions as imposed by the
		  classical analytical approach. Yet candidate programs are
		  computed based on analytical techniques from the examples
		  instead of being generated independently from the examples.
		  A prototypical implementation shows first that programs are
		  inducible which are not in scope of classical purely
		  analytical techniques and second that the induction times
		  are shorter than in recent generate-and-test based
		  methods.},
  doi = {10.1007/978-3-642-00515-2_7},
  keywords = {analytical ip; constructor systems; ifp; igor2g;
		  induction; inductive programming; inproceedings; program
		  synthesis},
  annote = {first regularly published paper on {Igor2.2}},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/publications/lopstr08.pdf}
}
 
Emanuel Kitzelmann. Inductive reasoning operators in the program synthesis system Igor2. Submitted to Logic-based Program Synthesis and Transformation (LOPSTR'09, 19th International Symposium, Coimbra, Portugal, Sept.2009), 2009.
@unpublished{kitzelmann:2009b,
  author = {Emanuel Kitzelmann},
  title = {Inductive Reasoning Operators in the Program Synthesis
		  System {Igor2}},
  year = 2009,
  note = {Submitted to Logic-based Program Synthesis and
		  Transformation ({LOPSTR'09}, 19th International Symposium,
		  Coimbra, Portugal, Sept.\,2009)},
  keywords = {inductive programming},
  annote = {add on to ``Analytical Inductive Functional Programming''
		  (http://www.cogsys.wiai.uni-bamberg.de/publications/lopstr08pre.pdf))}
}
 
Emanuel Kitzelmann. Inductive programming: A survey of program synthesis techniques. In Ute Schmid, Emanuel Kitzelmann, and Rinus Plasmeijer, editors, Approaches and Applications of Inductive Programming. 3rd International Workshop, AAIP'09, Edinburgh, UK, Sept.4, 2009. Revised Papers, volume 5812 of Lecture Notes in Computer Science, pages 50-73, Berlin/Heidelberg, 2010. Springer.
@inproceedings{kitzelmann:2010,
  author = {Emanuel Kitzelmann},
  title = {Inductive Programming: A Survey of Program Synthesis
		  Techniques},
  editor = {Ute Schmid and Emanuel Kitzelmann and Rinus Plasmeijer},
  booktitle = {Approaches and Applications of Inductive Programming. 3rd
		  International Workshop, {AAIP'09}, Edinburgh, UK, Sept.\,4,
		  2009. Revised Papers},
  year = 2010,
  series = {Lecture Notes in Computer Science},
  volume = 5812,
  pages = {50--73},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-11930-9},
  url = {http://www.springerlink.com/content/740664m804634k04/},
  abstract = {Inductive programming (IP)the use of inductive reasoning
		  methods for programming, algorithm design, and software
		  developmentis a currently emerging research field. A major
		  subfield is inductive program synthesis, the
		  (semi-)automatic construction of programs from exemplary
		  behavior. Inductive program synthesis is not a unified
		  research field until today but scattered over several
		  different established research fields such as machine
		  learning, inductive logic programming, genetic programming,
		  and functional programming. This impedes an exchange of
		  theory and techniques and, as a consequence, a progress of
		  inductive programming. In this paper we survey theoretical
		  results and methods of inductive program synthesis that
		  have been developed in different research fields until
		  today.},
  keywords = {inductive programming},
  doi = {10.1007/978-3-642-11931-6_3},
  documenturl = {http://www.springerlink.com/content/740664m804634k04/fulltext.pdf}
}
 
Emanuel Kitzelmann, Ute Schmid, Martin Mühlpfordt, and Fritz Wysotzki. Folding of finite program terms to recursive program schemes. In T. Sadam and V. Sgure, editors, Intelligent Systems. Proceedings of the 1st International IEEE Symposium (Varna, Bulgaria, Sept.10-12, 2002), volume 1, pages 144-149. IEEE Press, 2002.
@inproceedings{kitzelmann_ea:2002,
  author = {Emanuel Kitzelmann and Ute Schmid and Martin
		  M{\"u}hlpfordt and Fritz Wysotzki},
  title = {Folding of finite program terms to recursive program
		  schemes},
  editor = {T. Sadam and V. Sgure},
  booktitle = {Intelligent Systems. Proceedings of the 1st International
		  {IEEE} Symposium (Varna, Bulgaria, Sept.\,10--12, 2002)},
  year = 2002,
  volume = 1,
  pages = {144--149},
  publisher = {IEEE Press},
  abstract = {We present an approach to inductive synthesis of
		  functional programs based on the detection of recurrence
		  relations. A given term is considered as the k-th unfolding
		  of an unknown recursive program. If a recurrence relations
		  can be identified in the term, it can be folded into a
		  recursive program which: (a) can reproduce the term and (b)
		  generalizes over it. Our approach goes beyond Summers'
		  classical approach (1977) in several aspects: it is
		  language independent and works for terms belonging to an
		  arbitrary term algebra; it allows induction of sets of
		  recursive equations which are in some arbitrary `calls'
		  relation; induced equations can be dependent on more than
		  one input parameters and we can detect interdependencies of
		  variable substitutions in recursive calls; the given input
		  terms can represent incomplete unfoldings of an
		  hypothetical recursive program.},
  annote = {Welcome to IEEE Xplore 2.0: Folding of finite program
		  terms to recursive program schemes},
  isbn = {0-7803-7134-8},
  keywords = {2002; automatic programming; functional programming;
		  igor1; induction; inductive; inductive functional
		  programming; inductive inference; inductive program
		  synthesis; inductive programming; inproceedings; myown;
		  programming; published; recursive program schemes},
  url = {http://dx.doi.org/10.1109/IS.2002.1044245}
}
 
Emanuel Kitzelmann, Ute Schmid, Martin Mühlpfordt, and Fritz Wysotzki. Inductive synthesis of functional programs. In J. Calmet, B. Benhamou, O. Caprotti, L. Henocque, and V. Sorge, editors, Artificial Intelligence, Automated Reasoning, and Symbolic Computation. Joint International Conferences AISC'02 and Calculemus'02, Marseille, France, July1-5, 2002. Proceedings, volume 2385 of Lecture Notes in Computer Science, pages 337-354, Berlin/Heidelberg, 2002. Springer.
@inproceedings{kitzelmann_ea:2002b,
  author = {Emanuel Kitzelmann and Ute Schmid and Martin
		  M{\"u}hlpfordt and Fritz Wysotzki},
  title = {Inductive Synthesis of Functional Programs},
  editor = {Calmet, J. and Benhamou, B. and Caprotti, O. and Henocque,
		  L. and Sorge, V.},
  booktitle = {Artificial Intelligence, Automated Reasoning, and Symbolic
		  Computation. Joint International Conferences {AISC'02} and
		  {Calculemus'02}, Marseille, France, July\,1--5, 2002.
		  Proceedings},
  year = 2002,
  series = {Lecture Notes in Computer Science},
  volume = 2385,
  pages = {337--354},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-43865-6},
  url = {http://www.springerlink.com/content/r02frg6bh82g29pw/},
  doi = {10.1007/3-540-45470-5_6},
  abstract = {We present an approach to folding of finite program terms
		  based on the detection of recurrence relations in a single
		  given term which is considered as the k-th unfolding of an
		  unknown recursive program. Our approach goes beyond
		  Summers' classical approach in several aspects: It is
		  language independent and works for terms belonging to an
		  arbitrary term algebra; it allows induction of sets of
		  recursive equations which are in some arbitrary ``calls''
		  relation; induced equations can be dependent on more than
		  one input parameters and we can detect interdependencies of
		  variable substitutions in recursive calls; the given input
		  terms can represent incomplete unfoldings of an
		  hypothetical recursive program.},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/publications/JMLR-05-164-1.pdf},
  keywords = {2002; automatic programming; functional programming;
		  igor1; induction; inductive; inductive functional
		  programming; inductive inference; inductive program
		  synthesis; inductive programming; inproceedings; myown;
		  programming; published; recursive program schemes}
}
 
Emanuel Kitzelmann, Roland J. Olsson, and Ute Schmid, editors. AAIP'05: Proceedings of the 1st Workshop on Approaches and Applications of Inductive Programming (Bonn, Germany, Aug.7, 2005), 2005. In conjunction with the 22nd International Conference on Machine Learning (ICML'05).
@proceedings{kitzelmann_ea:2005,
  title = {{AAIP'05}: Proceedings of the 1st Workshop on Approaches
		  and Applications of Inductive Programming (Bonn, Germany,
		  Aug.\,7, 2005)},
  year = 2005,
  editor = {Emanuel Kitzelmann and Roland J. Olsson and Ute Schmid},
  note = {In conjunction with the 22nd International Conference on
		  Machine Learning ({ICML'05})},
  url = {http://www.cogsys.wiai.uni-bamberg.de/aaip05/index.html},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip05/proceedings/proceedings.pdf},
  size = {81 pages}
}
 
Timo Knuutila and Magnus Steinby. The inference of tree languages from finite samples: an algebraic approach. Theoretical Computer Science, 129(2):337-367, 1994.
@article{knuutila/steinby:1994,
  author = {Timo Knuutila and Magnus Steinby},
  title = {The inference of tree languages from finite samples: an
		  algebraic approach},
  journal = {Theoretical Computer Science},
  year = 1994,
  volume = 129,
  number = 2,
  pages = {337--367}
}
 
Yves Kodratoff and J. Fargues. A sane algorithm for the synthesis of LISP functions from example problems: The Boyer and Moore algorithm. In Derek H. Sleeman, editor, Proceedings of AISB/GI Conference (Hamburg, Germany, July 18-20, 1978, pages 169-175. Leeds University, 1978. Now ECAI: Proceedings of the 4th European Conference on Artificial Intelligence.
@inproceedings{kodratoff/fargues:1978,
  author = {Yves Kodratoff and J. Fargues},
  title = {A Sane Algorithm for the Synthesis of {LISP} Functions
		  from Example Problems: The {Boyer} and {Moore} Algorithm},
  editor = {Derek H. Sleeman},
  booktitle = {Proceedings of {AISB}/{GI} Conference (Hamburg, Germany,
		  July \,18--20, 1978},
  year = 1978,
  pages = {169--175},
  publisher = {Leeds University},
  note = {Now {ECAI}: Proceedings of the 4th European Conference on
		  Artificial Intelligence},
  keywords = {analytical ip; ifp; induction; inductive programming;
		  lisp; program synthesis}
}
 
Yves Kodratoff. A class of functions synthesized from a finite number of examples and a LISP program scheme. International Journal of Parallel Programming, 8(6):489-521, December 1979.
@article{kodratoff:1979,
  author = {Yves Kodratoff},
  title = {A Class of Functions Synthesized from a Finite Number of
		  Examples and a {LISP} Program Scheme},
  journal = {International Journal of Parallel Programming},
  year = 1979,
  volume = 8,
  number = 6,
  pages = {489--521},
  month = {December},
  publisher = {Springer},
  address = {Netherlands},
  issn = {0885-7458 (Print) 1573-7640 (Online)},
  url = {http://www.springerlink.com/content/x05l288304g8vk94/},
  doi = {10.1007/BF00995500},
  keywords = {Difference equation; fixed point semantics;
		  generalization; instantiation (giving a particular value to
		  the variables of a program scheme); Lisp programs; pattern
		  matching; program proof; program synthesis},
  abstract = {We define a class of functions that can be synthesized
		  from example problems. The algorithmic representation of
		  these functions is the interpretation of a given scheme.
		  The instantiation of the scheme variables is realized by a
		  new method which uses pattern matching then if necessary
		  generalization and further pattern matching. One can
		  compute the number of examples necessary to characterize in
		  a unique way a function of this class.}
}
 
Yves Kodratoff, Marta Franova, and Derek Partridge. Why and how program synthesis? In Klaus P. Jantke, editor, Analogical and Inductive Inference. International Workshop AII'89, Reinhardsbrunn Castle, GDR, Oct.1-6, 1989. Proceedings, volume 397 of Lecture Notes in Computer Science, pages 45-59, Berlin/Heidelberg, 1989. Springer.
@inproceedings{kodratoff_ea:1989,
  author = {Yves Kodratoff and Marta Franova and Derek Partridge},
  title = {Why and how program synthesis?},
  editor = {Jantke, Klaus P.},
  booktitle = {Analogical and Inductive Inference. International Workshop
		  {AII'89}, Reinhardsbrunn Castle, GDR, Oct.\,1--6, 1989.
		  Proceedings},
  year = 1989,
  series = {Lecture Notes in Computer Science},
  volume = 397,
  pages = {45--59},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-51734-4},
  url = {http://www.springerlink.com/content/78877816912005t8/},
  abstract = {Among the several misunderstandings about Program
		  Synthesis (PS), we particularly examine the one relative to
		  Logic Programming alleged to have solve this problem. Even
		  though theoretical reasons are well-known, we provide a
		  detailed analysis of the practical reasons why a formal
		  specification may be hard to program in PROLOG. All that
		  contributes to the clarification of the exact role of PS in
		  AI and in Software Engineering, and its possible
		  application to software certification.},
  keywords = {program synthesis from formal specifications; inductive
		  theorem proving; certification cycle},
  doi = {10.1007/3-540-51734-0_51},
  annote = {ute-inflit}
}
 
Pieter W. M. Koopman and Rinus Plasmeijer. Generic generation of the elements of data types. In Marko C. J. D. van Eekelen, editor, TFP'05: Revised Selected Papers from the 6th Symposium on Trends in Functional Programming (Tallinn, Estonia, Sep.23-24, 2005), volume 6 of Trends in Functional Programming, pages 163-178. Intellect, 2007.
@inproceedings{koopman/plasmeijer:2007,
  author = {Pieter W. M. Koopman and Rinus Plasmeijer},
  title = {Generic generation of the elements of data types},
  editor = {Marko C. J. D. van Eekelen},
  booktitle = {{TFP'05}: Revised Selected Papers from the 6th Symposium
		  on Trends in Functional Programming (Tallinn, Estonia,
		  Sep.\,23--24, 2005)},
  year = 2007,
  series = {Trends in Functional Programming},
  volume = 6,
  pages = {163--178},
  publisher = {Intellect},
  isbn = {978-1-84150-176-5}
}
 
Pieter W. M. Koopman and Rinus Plasmeijer. Systematic synthesis of functions. In Henrik Nilsson, editor, TFP'06: Revised Selected Papers from the 7th Symposium on Trends in Functional Programming (Nottingham, United Kingdom, April19-21, 2006, volume 7 of Trends in Functional Programming, pages 35-54. Intellect, 2007.
@inproceedings{koopman/plasmeijer:2007b,
  author = {Pieter W. M. Koopman and Rinus Plasmeijer},
  title = {Systematic Synthesis of Functions},
  editor = {Henrik Nilsson},
  booktitle = {{TFP'06}: Revised Selected Papers from the 7th Symposium
		  on Trends in Functional Programming (Nottingham, United
		  Kingdom, April\,19--21, 2006},
  year = 2007,
  series = {Trends in Functional Programming},
  volume = 7,
  pages = {35--54},
  publisher = {Intellect},
  isbn = {978-1-84150-188-8},
  documenturl = {http://www.cs.nott.ac.uk/~nhn/TFP2006/Papers/13-KoopmanPlasmeijer-SystematicSynthesisOfFunctions.pdf},
  keywords = {automated testing; enumerative ip; ifp; induction;
		  inductive programming; inproceedings; program synthesis}
}
 
Pieter Koopman and Rinus Plasmeijer. Synthesis of functions using generic programming. In Ute Schmid, Emanuel Kitzelmann, and Rinus Plasmeijer, editors, Approaches and Applications of Inductive Programming. 3rd International Workshop, AAIP'09, Edinburgh, UK, Sept.4, 2009. Revised Papers, volume 5812 of Lecture Notes in Computer Science, pages 25-49, Berlin/Heidelberg, 2010. Springer.
@inproceedings{koopman/plasmeijer:2010,
  author = {Pieter Koopman and Rinus Plasmeijer},
  title = {Synthesis of Functions Using Generic Programming},
  editor = {Ute Schmid and Emanuel Kitzelmann and Rinus Plasmeijer},
  booktitle = {Approaches and Applications of Inductive Programming. 3rd
		  International Workshop, {AAIP'09}, Edinburgh, UK, Sept.\,4,
		  2009. Revised Papers},
  year = 2010,
  series = {Lecture Notes in Computer Science},
  volume = 5812,
  pages = {25--49},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-11930-9},
  url = {http://www.springerlink.com/content/g532p3m741r10373/},
  abstract = {This paper describes a very flexible way to synthesize
		  functions matching a given predicate. This can be used to
		  find general recursive functions or},
  doi = {10.1007/978-3-642-11931-6_2},
  keywords = {gast; inductive programming},
  documenturl = {http://www.springerlink.com/content/g532p3m741r10373/fulltext.pdf}
}
 
Pieter Koopman, Artem Alimarine, Jan Tretmans, and Rinus Plasmeijer. GAST: Generic automated software testing. In Implementation of Functional Languages. 14th International Workshop, IFL'02, Madrid, Spain, Sept.16-18, 2002. Revised Selected Papers, volume 2670 of Lecture Notes in Computer Science, pages 84-100, Berlin/Heidelberg, 2003. Springer.
@inproceedings{koopman_ea:2003,
  author = {Pieter Koopman and Artem Alimarine and Jan Tretmans and
		  Rinus Plasmeijer},
  title = {{GAST}: Generic Automated Software Testing},
  booktitle = {Implementation of Functional Languages. 14th International
		  Workshop, {IFL'02}, Madrid, Spain, Sept.\,16--18, 2002.
		  Revised Selected Papers},
  year = 2003,
  series = {Lecture Notes in Computer Science},
  volume = 2670,
  pages = {84--100},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-40190-2},
  url = {http://www.springerlink.com/content/tt9u30x8wdal4d41/},
  doi = {10.1007/3-540-44854-3_6},
  keywords = {ase; automated testing; gast; software engineering;
		  theorem proving},
  abstract = {Software testing is a labor-intensive, and hence
		  expensive, yet heavily used technique to control quality.
		  In this paper we introduce Gast, a fully automatic test
		  tool. Properties about functions and datatypes can be
		  expressed in first order logic. Gast automaticallyand
		  systematically generates appropriate test data, evaluates
		  the property for these values, and analyzes the test
		  results.This makes it easier and cheaper to test software
		  components. The distinguishing property of our system is
		  that the test dataare generated in a systematic and generic
		  way using generic programming techniques. This implies that
		  there is no need forthe user to indicate how data should be
		  generated. Moreover, duplicated tests are avoided, and for
		  finite domains Gast isable to prove a property by testing
		  it for all possible values. As an important side-effect, it
		  also encourages stating formalproperties of the software.}
}
 
Richard E. Korf. Macro-operators: A weak method for learning. Artificial Intelligence, 26(1):35-77, 1985.
@article{korf:1985,
  author = {Richard E. Korf},
  title = {Macro-Operators: A Weak Method for Learning},
  journal = {Artificial Intelligence},
  year = 1985,
  volume = 26,
  number = 1,
  pages = {35--77},
  keywords = {macro-operators}
}
 
John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.
@book{koza:1992,
  author = {John R. Koza},
  title = {Genetic Programming: {O}n the Programming of Computers by
		  Means of Natural Selection},
  publisher = {MIT Press},
  year = 1992,
  address = {Cambridge, MA, USA},
  keywords = {enumerative ip; gp; induction; inductive programming;
		  program evolution; program synthesis},
  isbn = {0-262-11170-5}
}
 
John R. Koza, David Andre, Forrest H. Bennett, and Martin A. Keane. Genetic Programming III: Darwinian Invention & Problem Solving. Morgan Kaufmann, San Francisco, CA, USA, 1999.
@book{koza_ea:1999,
  author = {John R. Koza and David Andre and Forrest H. Bennett and
		  Martin A. Keane},
  title = {Genetic Programming III: Darwinian Invention \& Problem
		  Solving},
  publisher = {Morgan Kaufmann},
  year = 1999,
  address = {San Francisco, CA, USA},
  keywords = {adr; enumerative ip; gp; induction; inductive programming;
		  program evolution; program synthesis},
  isbn = {1558605436}
}
 
J. Krems, Ute Schmid, and Fritz Wysotzki, editors. ECCM'96: Proceedings of the 1st European Workshop on Cognitive Modelling (Berlin, Germany, Nov.14-16, 1996), volume 96 of Forschungsberichte des Fachbereichs Informatik, TU Berlin, 1996.
@proceedings{krems_ea:1996,
  title = {{ECCM'96}: Proceedings of the 1st European Workshop on
		  Cognitive Modelling (Berlin, Germany, Nov.\,14--16, 1996)},
  year = 1996,
  editor = {J. Krems and Ute Schmid and Fritz Wysotzki},
  volume = 96,
  number = 39,
  series = {Forschungsberichte des Fachbereichs Informatik},
  address = {TU Berlin}
}
 
R. L. Kruse. On teaching recursion. ACM SIGCCE-Bulletin, 14:92-96, 1982.
@article{kruse:1982,
  author = {R. L. Kruse},
  title = {On teaching recursion},
  journal = {{ACM} {SIGCCE}-Bulletin},
  year = 1982,
  volume = 14,
  pages = {92--96}
}
 
D. M. Kurland and R. D. Pea. Children's mental models of recursive logo programs. Journal of Educational Computing Research, 1(2):235-243, 1985.
@article{kurland/pea:1985,
  author = {D. M. Kurland and R. D. Pea},
  title = {Children's Mental Models of Recursive Logo Programs},
  journal = {Journal of Educational Computing Research},
  year = 1985,
  volume = 1,
  number = 2,
  pages = {235--243}
}
 
Steffen Lange. A program synthesis algorithm exemplified. In W. Bibel and K.P. Jantke, editors, Mathematical Methods of Specification and Synthesis of Software Systems '85. Proceedings of the International Spring School Wendisch-Rietz, GDR, April22-26, 1985, volume 215 of Lecture Notes in Computer Science, pages 185-193, Berlin/Heidelberg, 1986. Springer.
@inproceedings{lange:1986,
  author = {Steffen Lange},
  title = {A program synthesis algorithm exemplified},
  editor = {W. Bibel and K.P. Jantke},
  booktitle = {Mathematical Methods of Specification and Synthesis of
		  Software Systems '85. Proceedings of the International
		  Spring School Wendisch-Rietz, GDR, April\,22--26, 1985},
  year = 1986,
  series = {Lecture Notes in Computer Science},
  volume = 215,
  pages = {185--193},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-16444-9},
  url = {http://www.springerlink.com/content/y7nl3x717553h736/},
  abstract = {We present a algorithm for synthesizing programs from
		  input/output examples of their behavior. This method is a
		  prototype of a feasible inductive inference algorithm. It
		  is able to synthesize programs from a considerably small
		  number of examples, which, in fact, provide only incomplete
		  information, in general. The main computational work
		  performed during the synthesis process consists in
		  deducations of term equations and inequalities. The
		  investigated synthesis algorithm is well-structured and
		  assumes some basic knowledge formalized as a heterogeneous
		  signature with some first order axioms. We introduce this
		  synthesis algorithm in detail by means of a particular
		  program for a sorting algorithm.},
  doi = {10.1007/3-540-16444-8_15}
}
 
Pat Langley and Dongkyu Choi. Learning recursive control programs from problem solving. Journal of Machine Learning Research, 7:493-518, 2006. Special Topic on Approaches and Applications of Inductive Programming.
@article{langley/choi:2006,
  author = {Pat Langley and Dongkyu Choi},
  title = {Learning Recursive Control Programs from Problem Solving},
  journal = {Journal of Machine Learning Research},
  year = 2006,
  volume = 7,
  pages = {493--518},
  note = {Special Topic on Approaches and Applications of Inductive
		  Programming},
  publisher = {MIT Press},
  editor = {Roland J. Olsson and Ute Schmid},
  url = {http://jmlr.csail.mit.edu/papers/v7/},
  documenturl = {http://www.jmlr.org/papers/volume7/langley06a/langley06a.pdf},
  keywords = {inductive programming; learning-and-planning; planning;
		  program synthesis; read}
}
 
Stéphane Lapointe and Stan Matwin. Sub-unification: a tool for efficient induction of recursive programs. In Derek Sleeman and Peter Edwards, editors, ML'92: Proceedings of the Ninth International Workshop on Machine Learning (Aberdeen, Scotland, United Kingdom, July1-3, 1992), pages 273-281, San Francisco, CA, USA, 1992. Morgan Kaufmann.
@inproceedings{lapointe/matwin:1992,
  author = {St\'{e}phane Lapointe and Stan Matwin},
  title = {Sub-unification: a Tool for Efficient Induction of
		  Recursive Programs},
  editor = {Derek Sleeman and Peter Edwards},
  booktitle = {{ML'92}: Proceedings of the Ninth International Workshop
		  on Machine Learning (Aberdeen, Scotland, United Kingdom,
		  July\,1--3, 1992)},
  year = 1992,
  pages = {273--281},
  address = {San Francisco, CA, USA},
  publisher = {Morgan Kaufmann},
  url = {http://portal.acm.org/citation.cfm?id=141975.142038},
  keywords = {CRUSTACEAN; analytical ip; ilp; inductive programming;
		  ip-system; program synthesis; recursion},
  annote = {sub-unification, a technique to invert implication}
}
 
Stéphane Lapointe, Charles X. Ling, and Stan Matwin. Constructive inductive logic programming. In Ruzena Bajcsy, editor, IJCAI'93: Proceedings of the 13th International Joint Conference on Artificial Intelligence (Chambéry, France, Aug.28-Sep.3, 1993), pages 1030-1036. Morgan Kaufmann, 1993.
@inproceedings{lapointe_ea:1993,
  author = {St\'{e}phane Lapointe and Charles X. Ling and Stan Matwin},
  title = {Constructive Inductive Logic Programming},
  editor = {Ruzena Bajcsy},
  booktitle = {{IJCAI}'93: Proceedings of the 13th International Joint
		  Conference on Artificial Intelligence (Chamb\'ery, France,
		  Aug.\,28--Sep.\,3, 1993)},
  year = 1993,
  pages = {1030--1036},
  publisher = {Morgan Kaufmann}
}
 
N. Lavrač and S. Džeroski. Inductive Logic Programming. Techniques and Applications. Ellis Horwood, London, 1994.
@book{lavrac/dzeroski:1994,
  author = {N. Lavra\v{c} and S. D\v{z}eroski},
  title = {Inductive Logic Programming. Techniques and Applications},
  publisher = {Ellis Horwood},
  year = 1994,
  address = {London},
  annote = {ute-inflit}
}
 
Guillaume Le Blanc. Bmwk revisited generalization and formalization of an algorithm for detecting recursive relations in term sequences. In Machine Learning: ECML-94. European Conference on Machine Learning Catania, Italy, April6-8, 1994. Proceedings, volume 784 of Lecture Notes in Computer Science, pages 183-197, Berlin/Heidelberg, 1994. Springer.
@inproceedings{le-blanc:1994,
  author = {Le Blanc, Guillaume},
  title = {BMWk revisited generalization and formalization of an
		  algorithm for detecting recursive relations in term
		  sequences},
  booktitle = {Machine Learning: {ECML-94}. European Conference on
		  Machine Learning Catania, Italy, April\,6--8, 1994.
		  Proceedings},
  year = 1994,
  series = {Lecture Notes in Computer Science},
  volume = 784,
  pages = {183--197},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  abstract = {As several works in Machine Learning (particularly in
		  Inductive Logic Programming) have focused on building
		  recursive definitions from examples, this paper presents a
		  formalization and a generalization of the BMWk methodology,
		  which stems from program synthesis from examples, ten years
		  ago. The framework of the proposed formalization is term
		  rewriting. It allows to state some theoretical results on
		  the qualities and limitations of the method.},
  keywords = {analytical ip; ifp; induction; inductive programming;
		  program synthesis; term rewriting},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-57868-0},
  url = {http://www.springerlink.com/content/y0vv622028812821/},
  doi = {10.1007/3-540-57868-4_58}
}
 
G. Le Blanc. BMWk revisited: Generalization and formalization of an algorithm for detecting recursive relations in term sequences. In Francesco Bergadano and Luc De Raedt, editors, Machine Learning: ECML-94. European Conference on Machine Learning Catania, Italy, April6-8, 1994. Proceedings, volume 784 of Lecture Notes in Computer Science, pages 183-197, Berlin/Heidelberg, 1994. Springer.
@inproceedings{le-blanc:1994b,
  author = {G. {Le~Blanc}},
  title = {{BMW}k revisited: {Generalization} and formalization of an
		  algorithm for detecting recursive relations in term
		  sequences},
  editor = {Bergadano, Francesco and De~Raedt, Luc},
  booktitle = {Machine Learning: {ECML-94}. European Conference on
		  Machine Learning Catania, Italy, April\,6--8, 1994.
		  Proceedings},
  year = 1994,
  series = {Lecture Notes in Computer Science},
  volume = 784,
  pages = {183--197},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-57868-0},
  url = {http://www.springerlink.com/content/y0vv622028812821/},
  abstract = {As several works in Machine Learning (particularly in
		  Inductive Logic Programming) have focused on building
		  recursive definitions from examples, this paper presents a
		  formalization and a generalization of the BMWk methodology,
		  which stems from program synthesis from examples, ten years
		  ago. The framework of the proposed formalization is term
		  rewriting. It allows to state some theoretical results on
		  the qualities and limitations of the method.},
  doi = {10.1007/3-540-57868-4_58},
  annote = {ute-inflit}
}
 
C. Leinbach and A.L. Wijesinha. On classifying recursive algorithms. ACM SIGCCE-Bulletin, 18:186-190, 1986.
@article{leinbach/wijesinha:1986,
  author = {C. Leinbach and A.L. Wijesinha},
  title = {On classifying recursive algorithms},
  journal = {{ACM} {SIGCCE}-Bulletin},
  year = 1986,
  volume = 18,
  pages = {186--190}
}
 
Leonid A. Levin. Universal sequential search problems. Problems of Information Transmission, 9(3), 1973.
@article{levin:1973,
  author = {Leonid A. Levin},
  title = {Universal Sequential Search Problems},
  journal = {Problems of Information Transmission},
  year = 1973,
  volume = 9,
  number = 3,
  keywords = {universal search}
}
 
Xiang Li. Utilising Restricted For-Loops in Genetic Programming. PhD thesis, Royal Melbourne Institute of Technology, School of Computer Science and Information Technology, Melbourne, Victoria, Australia, 2007.
@phdthesis{li:2007,
  author = {Xiang Li},
  title = {Utilising Restricted For-Loops in Genetic Programming},
  school = {Royal Melbourne Institute of Technology, School of
		  Computer Science and Information Technology},
  year = 2007,
  address = {Melbourne, Victoria, Australia},
  documenturl = {http://goanna.cs.rmit.edu.au/~vc/papers/li-phd.pdf},
  keywords = {enumerative ip; gp; induction; inductive programming;
		  loops; program evolution; program synthesis}
}
 
Henry Lieberman. Programming descriptive analogies by example. In Workshop on Inheritance Hierarchies in Knowledge Representation (Viareggio, Italy, Feb.6-8, 1989), 1989.
@inproceedings{lieberman:1989,
  author = {Henry Lieberman},
  title = {Programming Descriptive Analogies by Example},
  booktitle = {Workshop on Inheritance Hierarchies in Knowledge
		  Representation (Viareggio, Italy, Feb.\,6--8, 1989)},
  year = 1989,
  annote = {ute-inflit}
}
 
H. Lieberman. Tinker: A programming by demonstration system for beginning programmers. In Alan Cypher, editor, Watch What I Do: Programming by Demonstration, chapter 2. MIT Press, Cambridge, MA, 1993.
@incollection{lieberman:1993,
  author = {H. Lieberman},
  title = {Tinker: {A} Programming by Demonstration System for
		  Beginning Programmers},
  editor = {Alan Cypher},
  booktitle = {Watch What {I} Do: {Programming} by Demonstration},
  publisher = {MIT Press},
  year = 1993,
  chapter = 2,
  address = {Cambridge, MA},
  documenturl = {http://www.acypher.com/wwid/Chapters/02Tinker.html},
  annote = {ute-inflit}
}
 
Henry Lieberman, editor. Your Wish is My Command: Programming by Example. Morgan Kaufmann, 2001.
@book{lieberman:2001,
  editor = {Henry Lieberman},
  title = {Your Wish is My Command: Programming by Example},
  publisher = {Morgan Kaufmann},
  year = 2001,
  keywords = {pbe}
}
 
Xiaofeng C. Ling. Inductive learning from good examples. In John Mylopoulos and Raymond Reiter, editors, IJCAI'91: Proceedings of the 12th International Joint Conference on Artificial Intelligence (Sydney, Australia, Aug.24-30, 1991), volume 2, pages 751-756. Morgan Kaufmann, 1991.
@inproceedings{ling:1991,
  author = {Xiaofeng C. Ling},
  title = {Inductive Learning from Good Examples},
  editor = {Mylopoulos, John and Reiter, Raymond},
  booktitle = {{IJCAI}'91: Proceedings of the 12th International Joint
		  Conference on Artificial Intelligence (Sydney, Australia,
		  Aug.\,24--30, 1991)},
  year = 1991,
  volume = 2,
  pages = {751--756},
  publisher = {Morgan Kaufmann},
  isbn = {1-55860-160-0},
  url = {http://dli.iiit.ac.in/ijcai/IJCAI-91-VOL2/CONTENT/content.htm},
  keywords = {ilp; inductive programming; learnability; machine
		  learning; program synthesis; recursion}
}
 
Moshe Looks. Competent Program Evolution. PhD thesis, Washington University in St. Louis, 2006.
@phdthesis{looks:2006,
  author = {Moshe Looks},
  title = {Competent Program Evolution},
  school = {Washington University in St. Louis},
  year = 2006,
  url = {http://metacog.org/doc.html},
  keywords = {cognition; enumerative ip; experiment; induction;
		  inductive programming; moses; program evolution; program
		  synthesis}
}
 
Moshe Looks. Scalable estimation-of-distribution program evolution. In Hod Lipson, editor, GECCO'07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (London, England, UK, July7-11, 2007), pages 539-546, New York, NY, USA, 2007. ACM. Session “Estimation of distribution algorithms”.
@inproceedings{looks:2007,
  author = {Moshe Looks},
  title = {Scalable Estimation-of-Distribution Program Evolution},
  editor = {Hod Lipson},
  booktitle = {{GECCO'07}: Proceedings of the 9th Annual Conference on
		  Genetic and Evolutionary Computation (London, England, UK,
		  July\,7--11, 2007)},
  year = 2007,
  pages = {539--546},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  note = {Session ``Estimation of distribution algorithms''},
  isbn = {978-1-59593-697-4},
  url = {http://doi.acm.org/10.1145/1276958.1277072},
  keywords = {cognition; enumerative ip; induction; inductive
		  programming; machine learning; moses; program evolution;
		  program synthesis},
  annote = {Scalable estimation-of-distribution program evolution}
}
 
H. R. Lu and K. S. Fu. Inferability of context-free programmed grammars. International Journal of Computer and Information Sciences, 13(1):33-58, February 1984.
@article{lu/fu:1984,
  author = {H. R. Lu and K. S. Fu},
  title = {Inferability of context-free programmed grammars},
  journal = {International Journal of Computer and Information
		  Sciences},
  year = 1984,
  volume = 13,
  number = 1,
  pages = {33--58},
  month = {February},
  issn = {0091-7036},
  keywords = {CFPG analysis; autocorrelation; context-free control
		  description; diagram; grammars; grammatical inferability
		  inference; language pattern programmed recognition; string
		  syntactic}
}
 
Haoru Lu and Ksun Fu. A general approach to inference of context-free programmed grammars. IEEE Transactions on Systems, Man, and Cybernetics, 14(2):191-202, 1984.
@article{lu/fu:1984b,
  author = {Haoru Lu and Ksun Fu},
  title = {A General Approach to Inference of Context-Free Programmed
		  Grammars},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics},
  year = 1984,
  volume = 14,
  number = 2,
  pages = {191--202},
  keywords = {language; matematics},
  annote = {ute-inflit}
}
 
Jianguo Lu and Jiafu Xu. Analogical program derivation based on type theory. Theoretical Computer Science, 113(2):259-272, June 1993.
@article{lu/xu:1993,
  author = {Jianguo Lu and Jiafu Xu},
  title = {Analogical program derivation based on type theory},
  journal = {Theoretical Computer Science},
  year = 1993,
  volume = 113,
  number = 2,
  pages = {259--272},
  month = {June},
  day = 7,
  url = {http://dx.doi.org/10.1016/0304-3975(93)90004-D},
  publisher = {Elsevier Science Publishers Ltd.},
  address = {Essex, UK},
  keywords = {analogical analogy derivation; development; formal
		  matching method; program reasoning; specification; theory
		  type; Programming and algorithm theory; Software
		  engineering techniques},
  issn = {0304-3975},
  school = {Institute of Computer Software, Nanjing University,
		  Nanjing 210008, People's Republic of China},
  abstract = {Our goal is to develop a formal method for analogically
		  deriving programs from past programming experience. It is
		  commonly recognized that program development plays a
		  central role in analogical programming. This paper proposes
		  to use a calculus to uniformly represent specification,
		  program, and the development from the former to the latter.
		  Thus analogical reasoning can be discussed in a single
		  framework. In this framework, we first propose an analogy
		  matching method to seek the analogical correspondence
		  between two specifications based on a generalization
		  procedure. Secondly, the analogical correspondence is used
		  as a basis for transforming existing program derivations to
		  new ones. The corresponding program can be obtained by
		  simple calculation of its type. Finally, an example is
		  given to illustrate our method.}
}
 
Donato Malerba. Learning recursive theories in the normal ILP setting. Fundamenta Informaticae, 57(1):39-77, 2003.
@article{malerba:2003,
  author = {Donato Malerba},
  title = {Learning Recursive Theories in the Normal {ILP} Setting},
  journal = {Fundamenta Informaticae},
  year = 2003,
  volume = 57,
  number = 1,
  pages = {39--77},
  address = {Amsterdam, The Netherlands},
  publisher = {IOS Press},
  issn = {0169-2968},
  url = {http://portal.acm.org/citation.cfm?id=1221518},
  keywords = {ATRE; ilp; inductive programming; ip-system; program
		  synthesis; recursion},
  abstract = {Induction of recursive theories in the normal ILP setting
		  is a difficult learning task whose complexity is equivalent
		  to multiple predicate learning. In this paper we propose
		  computational solutions to some relevant issues raised by
		  the multiple predicate learning problem. A
		  separate-and-parallel-conquer search strategy is adopted to
		  interleave the learning of clauses supplying predicates
		  with mutually recursive definitions. A novel generality
		  order to be imposed on the search space of clauses is
		  investigated, in order to cope with recursion in a more
		  suitable way. The consistency recovery is performed by
		  reformulating the current theory and by applying a layering
		  technique, based on the collapsed dependency graph. The
		  proposed approach has been implemented in the ILP system
		  ATRE and tested on some laboratory-sized and real-world
		  data sets. Experimental results demonstrate that ATRE is
		  able to learn correct theories autonomously and to discover
		  concept dependencies. Finally, related works and their main
		  differences with our approach are discussed.}
}
 
Zohar Manna and Richard Waldinger. Knowledge and reasoning in program synthesis. Artificial Intelligence, 6:175-208, 1975.
@article{manna/waldinger:1975,
  author = {Manna, Zohar and Waldinger, Richard},
  title = {Knowledge and reasoning in program synthesis},
  journal = {Artificial Intelligence},
  year = 1975,
  volume = 6,
  pages = {175--208}
}
 
Zohar Manna and Richard Waldinger. Synthesis: Dreams -> programs. IEEE Transactions on Software Engineering, 5(4):294-328, 1979.
@article{manna/waldinger:1979,
  author = {Manna, Zohar and Waldinger, Richard},
  title = {Synthesis: Dreams $\rightarrow$ Programs},
  journal = {IEEE Transactions on Software Engineering},
  year = 1979,
  volume = 5,
  number = 4,
  pages = {294--328}
}
 
Zohar Manna and Richard Waldinger. A deductive approach to program synthesis. ACM Transactions on Programming Languages and Systems, 2(1):90-121, 1980.
@article{manna/waldinger:1980,
  author = {Manna, Zohar and Waldinger, Richard},
  title = {A Deductive Approach to Program Synthesis},
  journal = {{ACM} Transactions on Programming Languages and Systems},
  year = 1980,
  volume = 2,
  number = 1,
  pages = {90--121},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  url = {http://doi.acm.org/10.1145/357084.357090},
  keywords = {article; ase; deductive program synthesis; program
		  synthesis},
  abstract = {Program synthesis is the systematic derivation of a
		  program from a given specification. A deductive approach to
		  program synthesis is presented for the construction of
		  recursive programs. This approach regards program synthesis
		  as a theorem-proving task and relies on a theorem-proving
		  method that combines the features of transformation rules,
		  unification, and mathematical induction within a single
		  framework.}
}
 
Zohar Manna and Richard Waldinger. How to clear a block: A theory of plans. Journal of Automated Reasoning, 3(4):343-378, December 1987.
@article{manna/waldinger:1987,
  author = {Manna, Zohar and Waldinger, Richard},
  title = {How to Clear a Block: A Theory of Plans},
  journal = {Journal of Automated Reasoning},
  year = 1987,
  volume = 3,
  number = 4,
  pages = {343--378},
  month = {December},
  publisher = {Kluwer Academic Publishers},
  keywords = {planning}
}
 
J. Marcinkowski and L. Pacholski. Undecidability of the horn-clause implication problem. In SFCS'92: Proceedings of the 33rd IEEE Annual Symposium on Foundations of Computer Science (Pittsburgh, PA, USA, Oct.24-27, 1992), pages 354-362. IEEE, 1992.
@inproceedings{marcinkowski/pacholski:1992,
  author = {J. Marcinkowski and L. Pacholski},
  title = {Undecidability of the Horn-clause Implication Problem},
  booktitle = {{SFCS'92}: Proceedings of the 33rd IEEE Annual Symposium
		  on Foundations of Computer Science (Pittsburgh, PA, USA,
		  Oct.\,24--27, 1992)},
  year = 1992,
  pages = {354--362},
  publisher = {IEEE},
  doi = {10.1109/SFCS.1992.267755},
  isbn = {0-8186-2900-2},
  keywords = {decidability; horn-clauses; logic},
  annote = {implication among Horn-clauses is not decidable},
  documenturl = {http://www.computer.org/plugins/dl/pdf/proceedings/focs/1992/2900/00/0267755.pdf}
}
 
Lionel Martin and Christel Vrain. A three-valued framework for the induction of general logic programs. In Luc De Raedt, editor, Advances in Inductive Logic Programming. IOS Press, 1996.
@incollection{martin/vrain:1996,
  author = {Lionel Martin and Christel Vrain},
  title = {A Three-valued Framework for the Induction of General
		  Logic Programs},
  editor = {De~Raedt, Luc},
  booktitle = {Advances in Inductive Logic Programming},
  publisher = {IOS Press},
  year = 1996,
  keywords = {ICN; ilp; inductive programming; ip-system; program
		  synthesis; recursion},
  annote = {technique to deal with recursion: recursive dependencies}
}
 
John McCarthy. Recursive functions of symbolic expressions and their computation by machine, part i. Communications of the ACM, 3(4):184-195, 1960.
@article{mccarthy:1960,
  author = {John McCarthy},
  title = {Recursive functions of symbolic expressions and their
		  computation by machine, Part I},
  journal = {Communications of the {ACM}},
  year = 1960,
  volume = 3,
  number = 4,
  pages = {184--195},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  url = {http://doi.acm.org/10.1145/367177.367199},
  documenturl = {http://portal.acm.org/ft_gateway.cfm?id=367199&type=pdf&coll=GUIDE&dl=GUIDE&CFID=96260185&CFTOKEN=23197999},
  keywords = {lisp},
  annote = {the original LISP paper}
}
 
James McDonald and John Anton. SPECWARE - producing software correct by construction. Technical Report KES.U.01.3., Kestrel Institute, 2001.
@techreport{mcdonald/anton:2001,
  author = {James McDonald and John Anton},
  title = {{SPECWARE} -- Producing Software Correct by Construction},
  institution = {Kestrel Institute},
  year = 2001,
  number = {KES.U.01.3.},
  keywords = {ase; deductive program synthesis; kestrel; program
		  synthesis; software engineering; specware; techreport}
}
 
Erik Meijer, Maarten Fokkinga, and Ross Paterson. Functional programming with bananas, lenses, envelopes and barbed wire. In Functional Programming Languages and Computer Architecture. 5th ACM Conference, Cambridge, MA, USA, Aug.26-30, 1991. Proceedings, volume 523 of Lecture Notes in Computer Science, pages 124-144, Berlin/Heidelberg, 1991. Springer.
@inproceedings{meijer_ea:1991,
  author = {Erik Meijer and Maarten Fokkinga and Ross Paterson},
  title = {Functional Programming with Bananas, Lenses, Envelopes and
		  Barbed Wire},
  booktitle = {Functional Programming Languages and Computer
		  Architecture. 5th {ACM} Conference, Cambridge, MA, USA,
		  Aug.\,26--30, 1991. Proceedings},
  year = 1991,
  series = {Lecture Notes in Computer Science},
  volume = 523,
  pages = {124--144},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-54396-1},
  url = {http://www.springerlink.com/content/77t3588175m4h5n7/},
  doi = {10.1007/3540543961_7},
  keywords = {catamorphisms; formal methods; functional programming;
		  higher-order functions; programming; recursion theory;
		  seminal paper},
  abstract = {We develop a calculus for lazy functional programming
		  based on recursion operators associated with data type
		  definitions. For these operators we derive various
		  algebraic laws that are useful in deriving and manipulating
		  programs. We shall show that all example functions in Bird
		  and Wadler's Introduction to Functional Programming can be
		  expressed using these operators.}
}
 
José Meseguer and Joseph A. Goguen. Initiality, induction, and computability. In Maurice Nivat and John C. Reynolds, editors, Algebraic Methods in Semantics, pages 459-541. Cambridge University Press, 1986.
@incollection{meseguer/goguen:1986,
  author = {Jos{\'e} Meseguer and Joseph A. Goguen},
  title = {Initiality, Induction, and Computability},
  editor = {Maurice Nivat and John C. Reynolds},
  booktitle = {Algebraic Methods in Semantics},
  publisher = {Cambridge University Press},
  year = 1986,
  pages = {459--541},
  keywords = {algebraic specification; equational logic; term
		  rewriting}
}
 
Ryszard S. Michalski and Robert E. Stepp. Learning from observation: Conceptual clustering. In Ryszard S. Michalski, Jaime G. Carbonell, and Tom M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, chapter 11, pages 331-364. Tioga, 1983.
@incollection{michalski/stepp:1983,
  author = {Ryszard S. Michalski and Robert E. Stepp},
  title = {Learning From Observation: Conceptual Clustering},
  editor = {Ryszard S. Michalski and Jaime G. Carbonell and Tom M.
		  Mitchell},
  booktitle = {Machine Learning: An Artificial Intelligence Approach},
  publisher = {Tioga},
  year = 1983,
  chapter = 11,
  pages = {331--364},
  keywords = {constructive induction; machine learning}
}
 
Tom M. Mitchell. The need for biases in learning generalizations. Technical report, Rutgers University, New Brunswick, NJ, 1980.
@techreport{mitchell:1980,
  author = {Tom M. Mitchell},
  title = {The Need for Biases in Learning Generalizations},
  institution = {Rutgers University},
  year = 1980,
  address = {New Brunswick, NJ},
  keywords = {machine learning}
}
 
Tom M. Mitchell. Generalization as search. Artificial Intelligence, 18(2):203-226, 1982.
@article{mitchell:1982,
  author = {Tom M. Mitchell},
  title = {Generalization as Search},
  journal = {Artificial Intelligence},
  year = 1982,
  volume = 18,
  number = 2,
  pages = {203--226},
  editor = {Daniel G. Bobrow},
  keywords = {machine learning; seminal paper},
  url = {http://dx.doi.org/10.1016/0004-3702(82)90040-6},
  abstract = {The problem of concept learning, or forming a general
		  description of a class of objects given a set of examples
		  and non-examples, is viewed here as a search problem.
		  Existing programs that generalize from examples are
		  characterized in terms of the classes of search strategies
		  that they employ. Several classes of search strategies are
		  then analyzed and compared in terms of their relative
		  capabilities and computational complexities.}
}
 
Thomas M. Mitchell. Machine Learning. McGraw-Hill, 1 edition, 1997.
@book{mitchell:1997,
  author = {Thomas M. Mitchell},
  title = {Machine Learning},
  publisher = {McGraw-Hill},
  year = 1997,
  edition = 1,
  url = {http://www.cs.cmu.edu/~tom/mlbook.html},
  keywords = {applications; book; induction; machine learning},
  isbn = 0070428077
}
 
Neil Mitchell. Deriving generic functions by example. In Jan Tobias Mühlberg and Juan Ignacio Perna, editors, Proceedings of the First York Doctoral Symposium 2007 (York, UK, Oct.26, 2007), pages 55-62. Department of Computer Science, University of York, UK, 2007. TechReport YCS-2007-421.
@inproceedings{mitchell:2007,
  author = {Neil Mitchell},
  title = {Deriving Generic Functions by Example},
  editor = {Jan Tobias M\"{u}hlberg and Juan Ignacio Perna},
  booktitle = {Proceedings of the First York Doctoral Symposium 2007
		  (York, UK, Oct.\,26, 2007)},
  year = 2007,
  pages = {55--62},
  publisher = {Department of Computer Science, University of York, UK},
  note = {Tech\,Report YCS-2007-421},
  documenturl = {http://community.haskell.org/~ndm/downloads/paper-deriving_generic_functions_by_example-26_oct_2007.pdf},
  abstract = {A function is said to be generic if it operates over
		  values of any data type. For example, a generic equality
		  function can test pairs of booleans, integers, lists, trees
		  etc. In most languages programmers must define generic
		  functions multiple times, specialised for each data type.
		  Alternatively, a tool could be used to specify the
		  relationship between the data type and the implementation,
		  but this relationship may be complex. This paper describes
		  a solution: given a single example of the generic function
		  on one data type, we can infer the relationship between a
		  data type and the implementation. We have used our method
		  in the Derive tool, allowing the implementation of 60\% of
		  the generic functions to be inferred.}
}
 
Neil Mitchell. Deriving a relationship from a single example. In Ute Schmid, Emanuel Kitzelmann, and Rinus Plasmeijer, editors, Approaches and Applications of Inductive Programming. 3rd International Workshop, AAIP'09, Edinburgh, UK, Sept.4, 2009. Revised Papers, volume 5812 of Lecture Notes in Computer Science, pages 1-24, Berlin/Heidelberg, 2010. Springer.
@inproceedings{mitchell:2010,
  author = {Neil Mitchell},
  title = {Deriving a Relationship from a Single Example},
  editor = {Ute Schmid and Emanuel Kitzelmann and Rinus Plasmeijer},
  booktitle = {Approaches and Applications of Inductive Programming. 3rd
		  International Workshop, {AAIP'09}, Edinburgh, UK, Sept.\,4,
		  2009. Revised Papers},
  year = 2010,
  series = {Lecture Notes in Computer Science},
  volume = 5812,
  pages = {1--24},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-11930-9},
  url = {http://www.springerlink.com/content/q764u283q955x810/},
  abstract = {Given an appropriate domain specific language (DSL), it is
		  possible to describe the relationship between Haskell data
		  types and many generic functions, typically type-class
		  instances. While describing the relationship is possible,
		  it is not always an easy task. There is an alternative
		  simply give one example output for a carefully chosen
		  input, and have the relationship derived.},
  doi = {10.1007/978-3-642-11931-6_1},
  documenturl = {http://www.springerlink.com/content/q764u283q955x810/fulltext.pdf}
}
 
Chowdhury R. Mofizur and Masayuki Numao. Top-down induction of recursive programs from small number of sparse examples. In Luc De Raedt, editor, Advances in Inductive Logic Programming. IOS Press, 1996.
@incollection{mofizur/numao:1996,
  author = {Chowdhury R. Mofizur and Masayuki Numao},
  title = {Top-down Induction of Recursive Programs from Small Number
		  of Sparse Examples},
  editor = {De~Raedt, Luc},
  booktitle = {Advances in Inductive Logic Programming},
  publisher = {IOS Press},
  year = 1996,
  keywords = {SMART; ilp; inductive programming; ip-system; program
		  synthesis; recursion}
}
 
Oleg Monakhov and Emilia Monakhova. Synthesis of scientific algorithms based on evolutionary computation and templates. In Emanuel Kitzelmann, Roland J. Olsson, and Ute Schmid, editors, AAIP'05: Proceedings of the 1st Workshop on Approaches and Applications of Inductive Programming (Bonn, Germany, Aug.7, 2005), pages 29-35, 2005. Full Paper.
@inproceedings{monakhov/monakhova:2005,
  author = {Oleg Monakhov and Emilia Monakhova},
  title = {Synthesis of Scientific Algorithms based on Evolutionary
		  Computation and Templates},
  editor = {Emanuel Kitzelmann and Roland J. Olsson and Ute Schmid},
  booktitle = {{AAIP'05}: Proceedings of the 1st Workshop on Approaches
		  and Applications of Inductive Programming (Bonn, Germany,
		  Aug.\,7, 2005)},
  year = 2005,
  pages = {29--35},
  note = {Full Paper},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip05/proceedings/mon-ip32.pdf}
}
 
Oleg Monakhov. Evolutionary synthesis of algorithms based on templates. Optoelectronics, Instrumentation and Data Processing, 2006.
@article{monakhov:2006,
  author = {Oleg Monakhov},
  title = {Evolutionary synthesis of algorithms based on templates},
  journal = {Optoelectronics, Instrumentation and Data Processing},
  year = 2006,
  publisher = {Allerton Press, Inc. distributed exclusively by Springer
		  Science+Business Media LLC},
  issn = {8756-6990 (Print) 1934-7944 (Online)},
  documenturl = {http://www.ict.nsc.ru/jct/getfile.php?id=775}
}
 
David J. Montana. Strongly typed genetic programming. Evolutionary Compututation, 3(2):199-230, 1995.
@article{montana:1995,
  author = {David J. Montana},
  title = {Strongly Typed Genetic Programming},
  journal = {Evolutionary Compututation},
  year = 1995,
  volume = 3,
  number = 2,
  pages = {199--230},
  address = {Cambridge, MA, USA},
  publisher = {MIT Press},
  url = {http://dx.doi.org/10.1162/evco.1995.3.2.199},
  keywords = {enumerative ip; gp; induction; inductive programming;
		  program evolution; program synthesis},
  abstract = {Genetic programming is a powerful method for automatically
		  generating computer programs via the process of natural
		  selection (Koza, 1992). However, in its standard form,
		  there is no way to restrict the programs it generates to
		  those where the functions operate on appropriate data
		  types. In the case when the programs manipulate multiple
		  data types and contain functions designed to operate on
		  particular data types, this can lead to unnecessarily large
		  search times and/or unnecessarily poor generalization
		  performance. Strongly typed genetic programming (STGP) is
		  an enhanced version of genetic programming that enforces
		  data-type constraints and whose use of generic functions
		  and generic data types makes it more powerful than other
		  approaches to type-constraint enforcement. After describing
		  its operation, we illustrate its use on problems in two
		  domains, matrix/vector manipulation and list manipulation,
		  which require its generality. The examples are (1) the
		  multidimensional least-squares regression problem, (2) the
		  multidimensional Kalman filter, (3) the list manipulation
		  function NTH, and (4) the list manipulation function
		  MAPCAR.}
}
 
Martin Mühlpfordt and Ute Schmid. Synthesis of recursive functions with interdependent parameters. In S. Lange and T. Zeugmann, editors, ALT'98: Proceedings of the satellite workshop on Applied Learning Theory (Kaiserslautern, Germany, Oct.7, 1998, 1998. Satellite workshop of the 9th International Conference on Algorithmic Learning Theory (Kaiserslautern, Rheinland-Pfalz, Germany, Oct.8-10, 1998).
@inproceedings{muehlpfordt/schmid:1998,
  author = {Martin M{\"u}hlpfordt and Ute Schmid},
  title = {Synthesis of recursive functions with interdependent
		  parameters},
  editor = {Lange, S. and Zeugmann, T.},
  booktitle = {{ALT'98}: Proceedings of the satellite workshop on Applied
		  Learning Theory (Kaiserslautern, Germany, Oct.\,7, 1998},
  year = 1998,
  note = {Satellite workshop of the 9th {International Conference on
		  Algorithmic Learning Theory} (Kaiserslautern,
		  Rheinland-Pfalz, Germany, Oct.\,8--10, 1998)}
}
 
Martin Mühlpfordt and Ute Schmid. Synthesis of recursive functions with interdependent parameters. In FGML'98: Proceedings of the Annual Meeting of the GI Machine Learning Group (Technische Universität, Berlin, Aug.17.-19, 1998), volume 98 of Forschungsberichte des Fachbereichs Informatik, pages 132-139, TU Berlin, 1998. Beiträge zum Treffen der GI-Fachgruppe 1.1.3 Maschinelles Lernen.
@inproceedings{muehlpfordt/schmid:1998b,
  author = {Martin M{\"u}hlpfordt and Ute Schmid},
  title = {Synthesis of recursive functions with interdependent
		  parameters},
  booktitle = {{FGML'98}: Proceedings of the {Annual Meeting of the GI
		  Machine Learning Group} (Technische Universit\"at, Berlin,
		  Aug.\,17.--19, 1998)},
  year = 1998,
  series = {Forschungsberichte des Fachbereichs Informatik},
  volume = 98,
  pages = {132--139},
  address = {TU Berlin},
  note = {Beitr{\"a}ge zum Treffen der GI-Fachgruppe 1.1.3
		  Maschinelles Lernen},
  number = 11
}
 
Martin Mühlpfordt. Syntaktische Inferenz Rekursiver Programmschemata. Diplomarbeit, Technische Universität Berlin, 2000.
@mastersthesis{muehlpfordt:2000,
  author = {Martin M\"{u}hlpfordt},
  title = {{Syntaktische Inferenz Rekursiver Programmschemata}},
  school = {Technische Universit\"{a}t Berlin},
  year = 2000,
  type = {Diplomarbeit},
  keywords = {igor1; inductive programming}
}
 
M. Müller and Ute Schmid. IPAL - a system that integrates problem solving, skill acquisition, and learning by analogy. In Ute Schmid, J. Krems, and Fritz Wysotzki, editors, ECCM'96: Proceedings of the 1st European Workshop on Cognitive Modelling (Berlin, Germany, Nov.14-16, 1996), volume 96 of Forschungsberichte des Fachbereichs Informatik, pages 246-247, TU Berlin, 1996.
@inproceedings{mueller/schmid:1996,
  author = {M. M{\"u}ller and Ute Schmid},
  title = {{IPAL} -- A system that integrates problem solving, skill
		  acquisition, and learning by analogy},
  editor = {Ute Schmid and J. Krems and Fritz Wysotzki},
  booktitle = {{ECCM'96}: Proceedings of the 1st European Workshop on
		  Cognitive Modelling (Berlin, Germany, Nov.\,14--16, 1996)},
  year = 1996,
  series = {Forschungsberichte des Fachbereichs Informatik},
  volume = 96,
  pages = {246--247},
  address = {TU Berlin},
  number = 39
}
 
Stephen H. Muggleton and Wray L. Buntine. Machine invention of first-order predicates by inverting resolution. In John E. Laird, editor, ICML'88: Proceedings of the 5th International Conference on Machine Learning (Ann Arbor, Michigan, USA, June12-14, 1988), pages 339-352. Morgan Kaufmann, 1988.
@inproceedings{muggleton/buntine:1988,
  author = {Stephen H. Muggleton and Wray L. Buntine},
  title = {Machine Invention of First-order Predicates by Inverting
		  Resolution},
  editor = {John E. Laird},
  booktitle = {{ICML'88}: Proceedings of the 5th International Conference
		  on Machine Learning (Ann Arbor, Michigan, USA,
		  June\,12--14, 1988)},
  year = 1988,
  pages = {339--352},
  publisher = {Morgan Kaufmann},
  isbn = {0-934613-64-8},
  keywords = {CIGOL; ilp; inductive programming; ip-system; predicate
		  invention; program synthesis}
}
 
Stephen H. Muggleton and Luc De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19, 20:629-679, May/July 1994. 10th Birthday Special Issue of the Journal of Logic Programming.
@article{muggleton/de-raedt:1994,
  author = {Muggleton, Stephen H. and De~Raedt, Luc},
  title = {Inductive logic programming: {Theory} and methods},
  journal = {Journal of Logic Programming},
  year = 1994,
  volume = {19, 20},
  pages = {629--679},
  month = {May\,/\,July},
  note = {10th Birthday Special Issue of the Journal of Logic
		  Programming},
  documenturl = {http://www.doc.ic.ac.uk/~shm/Papers/lpj.pdf},
  doi = {10.1016/0743-1066(94)90035-3}
}
 
Stephen H. Muggleton and C. Feng. Efficient induction of logic programs. In ALT'90: Proceedings of the 1st International Conference on Algorithmic Learning Theory (Tokyo, Japan, Oct.8-10, 1990), pages 368-381. Ohmsha, 1990.
@inproceedings{muggleton/feng:1990,
  author = {Stephen H. Muggleton and C. Feng},
  title = {Efficient Induction of Logic Programs},
  booktitle = {{ALT'90}: Proceedings of the 1st {International Conference
		  on Algorithmic Learning Theory} (Tokyo, Japan, Oct.\,8--10,
		  1990)},
  year = 1990,
  pages = {368--381},
  publisher = {Ohmsha},
  documenturl = {http://www.doc.ic.ac.uk/~shm/Papers/alt90.pdf},
  keywords = {analytical ip; applications; golem; ilp; induction;
		  inductive programming; inproceedings; machine learning;
		  program synthesis},
  annote = {golem, efficient rlgg, determinate terms, variable depth}
}
 
Stephen H. Muggleton and J. Firth. CProgol4.4: a tutorial introduction. In Saso Dzeroski and Nada Lavrac, editors, Relational Data Mining, pages 160-188. Springer, 2001.
@incollection{muggleton/firth:2001,
  author = {Stephen H. Muggleton and J. Firth},
  title = {{CP}rogol4.4: a tutorial introduction},
  editor = {Dzeroski, Saso and Lavrac, Nada},
  booktitle = {Relational Data Mining},
  publisher = {Springer},
  year = 2001,
  pages = {160--188},
  isbn = {978-3-540-42289-1}
}
 
Stephen H. Muggleton and C. D. Page. Self-saturation of definite clauses. In S. Wrobel, editor, ILP'94: Proceedings of the 4th International Workshop on Inductive Logic Programming (Bonn, Germany, Sept.12-14, 1994), volume 237 of GMD-Studien, pages 161-174. Gesellschaft für Mathematik und Datenverarbeitung MBH, 1994.
@inproceedings{muggleton/page:1994,
  author = {Stephen H. Muggleton and C. D. Page},
  title = {Self-saturation of Definite Clauses},
  editor = {S. Wrobel},
  booktitle = {{ILP'94}: Proceedings of the 4th International Workshop on
		  Inductive Logic Programming (Bonn, Germany, Sept.\,12--14,
		  1994)},
  year = 1994,
  series = {{GMD}-Studien},
  volume = 237,
  pages = {161--174},
  publisher = {{G}esellschaft f{\"{u}}r {M}athematik und
		  {D}atenverarbeitung {MBH}}
}
 
Stephen H. Muggleton. Duce, an oracle-based approach to constructive induction. In IJCAI'87: Proceedings of the 10th International Joint Conference on Artificial Intelligence (Milan, Italy, August23-28, 1987), volume 1, pages 287-292. Morgan Kaufmann, 1987.
@inproceedings{muggleton:1987,
  author = {Stephen H. Muggleton},
  title = {Duce, an Oracle-Based Approach to Constructive Induction},
  booktitle = {{IJCAI'87}: Proceedings of the 10th International Joint
		  Conference on Artificial Intelligence (Milan, Italy,
		  August\,23--28, 1987)},
  year = 1987,
  volume = 1,
  pages = {287--292},
  publisher = {Morgan Kaufmann},
  keywords = {constructive induction; duce; ilp; inductive programming}
}
 
Stephen H. Muggleton. Inductive logic programming. New Generation Computing, 8(4):295-318, 1991.
@article{muggleton:1991,
  author = {Stephen H. Muggleton},
  title = {Inductive Logic Programming},
  journal = {New Generation Computing},
  year = 1991,
  volume = 8,
  number = 4,
  pages = {295--318},
  annote = {muggleton introduces the term ``inductive logic
		  programming''; he defines ilp as intersection of machine
		  learning and computational logic; seminal paper on ILP},
  keywords = {article; ilp; induction; machine learning}
}
 
Stephen H. Muggleton. Inductive logic programming: Derivations, successes and shortcomings. SIGART Bulletin, 5(1):5-11, 1994.
@article{muggleton:1994,
  author = {Stephen H. Muggleton},
  title = {Inductive Logic Programming: Derivations, Successes and
		  Shortcomings},
  journal = {SIGART Bulletin},
  year = 1994,
  volume = 5,
  number = 1,
  pages = {5--11},
  documenturl = {http://www.doc.ic.ac.uk/~shm/Papers/sigart.pdf},
  keywords = {article; ilp; induction; machine learning}
}
 
Stephen H. Muggleton. Bayesian inductive logic programming. In COLT'94: Proceedings of the 7th Annual Conference on Computational Learning Theory (New Brunswick, NJ, USA, July12-15, 1994), pages 3-11. ACM, 1994.
@inproceedings{muggleton:1994b,
  author = {Stephen H. Muggleton},
  title = {Bayesian Inductive Logic Programming},
  booktitle = {{COLT'94}: Proceedings of the 7th Annual Conference on
		  Computational Learning Theory (New Brunswick, NJ, USA,
		  July\,12--15, 1994)},
  year = 1994,
  pages = {3--11},
  publisher = {{ACM}},
  keywords = {ilp}
}
 
Stephen H. Muggleton. Predicate invention and utilization. Journal of Experimental and Theoretical Artificial Intelligence, 6(1):121-130, 1994.
@article{muggleton:1994c,
  author = {Stephen H. Muggleton},
  title = {{Predicate invention and utilization}},
  journal = {Journal of Experimental and Theoretical Artificial
		  Intelligence},
  year = 1994,
  volume = 6,
  number = 1,
  pages = {121--130},
  publisher = {Taylor \& Francis},
  keywords = {ilp; induction; inductive programming; predicate
		  invention; program synthesis}
}
 
Stephen H. Muggleton. Inverse entailment and Progol. New Generation Computing, 13(3&4):245-286, 1995.
@article{muggleton:1995,
  author = {Stephen H. Muggleton},
  title = {Inverse Entailment and {P}rogol},
  journal = {New Generation Computing},
  year = 1995,
  volume = 13,
  number = {3\,\&\,4},
  pages = {245--286},
  publisher = {Ohmsha},
  documenturl = {http://www.doc.ic.ac.uk/~shm/Papers/InvEnt.ps.gz},
  keywords = {ilp; induction; inductive programming; ip-system;
		  learnability; machine learning; progol}
}
 
Stephen H. Muggleton. Inverting implication. In Machine Intelligence, volume 14. Oxford University Press, 1995.
@incollection{muggleton:1995b,
  author = {Stephen H. Muggleton},
  title = {Inverting implication},
  booktitle = {Machine Intelligence},
  publisher = {Oxford University Press},
  year = 1995,
  volume = 14
}
 
Stephen H. Muggleton. Learning from positive data. In Inductive Logic Programming. 6th International Workshop, ILP-96, Stockholm, Sweden, Aug.26-28, 1996. Selected Papers, volume 1314 of Lecture Notes in Computer Science, pages 358-376, Berlin/Heidelberg, 1997. Springer.
@inproceedings{muggleton:1997,
  author = {Stephen H. Muggleton},
  title = {Learning from Positive Data},
  booktitle = {Inductive Logic Programming. 6th International Workshop,
		  {ILP-96}, Stockholm, Sweden, Aug.\,26--28, 1996. Selected
		  Papers},
  year = 1997,
  series = {Lecture Notes in Computer Science},
  volume = 1314,
  pages = {358--376},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-63494-2},
  url = {http://www.springerlink.com/content/q762jww18178v480/},
  abstract = {Gold showed in 1967 that not even regular grammars can be
		  exactly identified from positive examples alone. Since it
		  is known that children learn natural grammars almost
		  exclusively from positives examples, Gold's result has been
		  used as a theoretical support for Chomsky's theory of
		  innate human linguistic abilities. In this paper new
		  results are presented which show that within a Bayesian
		  framework not only grammars, but also logic programs are
		  learnable with arbitrarily low expected error from positive
		  examples only. In addition, we show that the upper bound
		  for expected error of a learner which maximises the Bayes'
		  posterior probability when learning from positive examples
		  is within a small additive term of one which does the same
		  from a mixture of positive and negative examples. An
		  Inductive Logic Programming implementation is described
		  which avoids the pitfalls of greedy search by global
		  optimisation of this function during the local construction
		  of individual clauses of the hypothesis. Results of testing
		  this implementation on artificially-generated data-sets are
		  reported. These results are in agreement with the
		  theoretical predictions.},
  doi = {10.1007/3-540-63494-0_65},
  keywords = {1996; ILP; Muggleton; Progol; inductive logic programming;
		  inproceedings; inverse entailment; }
}
 
Stephen Muggleton. Learning the time complexity of logic programs. In Emanuel Kitzelmann, Roland J. Olsson, and Ute Schmid, editors, AAIP'05: Proceedings of the 1st Workshop on Approaches and Applications of Inductive Programming (Bonn, Germany, Aug.7, 2005), page 9, 2005. Invited Talk Abstract.
@inproceedings{muggleton:2005,
  author = {Stephen Muggleton},
  title = {Learning the Time Complexity of Logic Programs},
  editor = {Emanuel Kitzelmann and Roland J. Olsson and Ute Schmid},
  booktitle = {{AAIP'05}: Proceedings of the 1st Workshop on Approaches
		  and Applications of Inductive Programming (Bonn, Germany,
		  Aug.\,7, 2005)},
  year = 2005,
  pages = 9,
  note = {Invited Talk Abstract},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip05/proceedings/abs_muggleton.pdf}
}
 
Stephen H. Muggleton, C. H. Bryant, and A. Srinivasan. Learning Chomsky-like grammars for biological sequence families. In ICML'00: Proceedings of the 17th International Conference on Machine Learning (Stanford University, Stanford, CA, USA, June 29-July 2, 2000), pages 631-638. Morgan Kaufmann, 2000.
@inproceedings{muggleton_ea:2000,
  author = {Stephen H. Muggleton and C. H. Bryant and A. Srinivasan},
  title = {Learning {Chomsky}-like Grammars for Biological Sequence
		  Families},
  booktitle = {{ICML'00}: Proceedings of the 17th International
		  Conference on Machine Learning (Stanford University,
		  Stanford, CA, USA, June 29--July 2, 2000)},
  year = 2000,
  pages = {631--638},
  publisher = {Morgan Kaufmann},
  isbn = {1-55860-707-2}
}
 
Stephen H. Muggleton, Ramón P. Otero, and Alireza Tamaddoni-Nezhad, editors. Inductive Logic Programming. 16th International Conference, ILP'06, Santiago de Compostela, Spain, Aug.24-27, 2006, Revised Selected Papers, volume 4455 of Lecture Notes in Computer Science, Berlin/Heidelberg, 2007. Springer.
@proceedings{muggleton_ea:2007,
  title = {Inductive Logic Programming. 16th International
		  Conference, {ILP'06}, Santiago de Compostela, Spain,
		  Aug.\,24--27, 2006, Revised Selected Papers},
  year = 2007,
  editor = {Stephen H. Muggleton and Ram{\'o}n P. Otero and Alireza
		  Tamaddoni-Nezhad},
  volume = 4455,
  series = {Lecture Notes in Computer Science},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-73846-6},
  url = {http://www.springerlink.com/content/w77q4m266003/},
  doi = {10.1007/978-3-540-73847-3}
}
 
Claire Nédellec, Céline Rouveirol, Hilde Adé, Francesco Bergadano, and Birgit Tausend. Declarative bias in inductive logic programming. In Luc De Raedt, editor, Advances in Inductive Logic Programming. IOS Press, 1996.
@incollection{nedellec_ea:1996,
  author = {Claire N\'{e}dellec and C\'{e}line Rouveirol and Hilde
		  Ad\'{e} and Francesco Bergadano and Birgit Tausend},
  title = {Declarative Bias in Inductive Logic Programming},
  editor = {De~Raedt, Luc},
  booktitle = {Advances in Inductive Logic Programming},
  publisher = {IOS Press},
  year = 1996,
  keywords = {bias; declarative bias; ilp; induction; inductive
		  programming; program synthesis}
}
 
Jochen Nessel. Learnability of enumerable classes of recursive functions from “typical” examples. In Algorithmic Learning Theory. 10th International Conference, ALT'99, Tokyo, Japan, Dec.6-8, 1999. Proceedings, volume 1720 of Lecture Notes in Computer Science, pages 264-275, Berlin/Heidelberg, 2010. Springer.
@inproceedings{nessel:2010,
  author = {Jochen Nessel},
  title = {Learnability of Enumerable Classes of Recursive Functions
		  from ``Typical'' Examples},
  booktitle = {Algorithmic Learning Theory. 10th International
		  Conference, {ALT'99}, Tokyo, Japan, Dec.\,6--8, 1999.
		  Proceedings},
  year = 2010,
  series = {Lecture Notes in Computer Science},
  volume = 1720,
  pages = {264--275},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-66748-3},
  url = {http://www.springerlink.com/content/r1360773437p3450/},
  abstract = {The paper investigates whether it is possible to learn
		  every enumerable classes of recursive functions from
		  typical examples. Typical means, there is a computable
		  family of finite sets, such that for each function in the
		  class there isoneset of examples that can be used
		  inanysuitable hypothesis space for this class of functions.
		  As it will turn out, there are enumerable classes of
		  recursive functions that are not learnable from typical
		  examples. The learnable classes are characterized.},
  doi = {10.1007/3-540-46769-6_22}
}
 
Shan-Hwei Nienhuys-Cheng and Ronald de Wolf. Foundations of Inductive Logic Programming, volume 1228 of Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence. Springer, 1997.
@book{nienhuys-cheng/wolf:1997,
  author = {Shan-Hwei Nienhuys-Cheng and Ronald de Wolf},
  title = {Foundations of Inductive Logic Programming},
  publisher = {Springer},
  year = 1997,
  volume = 1228,
  series = {Lecture Notes in Computer Science. Lecture Notes in
		  Artificial Intelligence},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-62927-6},
  url = {http://portal.acm.org/citation.cfm?id=548817},
  doi = {10.1007/3-540-62927-0},
  keywords = {book; ilp; induction; inductive programming; logic}
}
 
Shan-Hwei Nienhuys-Cheng and Roland de Wolf. Subsumption Theorem and Refutation Completeness, volume 1228 of Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, chapter 5, pages 75-92. Springer, Berlin/Heidelberg, 1997.
@inbook{nienhuys-cheng/wolf:1997b,
  author = {Shan-Hwei Nienhuys-Cheng and Roland de Wolf},
  title = {Subsumption Theorem and Refutation Completeness},
  chapter = 5,
  pages = {75--92},
  publisher = {Springer},
  year = 1997,
  volume = 1228,
  series = {Lecture Notes in Computer Science. Lecture Notes in
		  Artificial Intelligence},
  address = {Berlin\,/\,Heidelberg},
  booktitle = {Foundations of Inductive Logic Programming},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-62927-6},
  url = {http://www.springerlink.com/content/x244q76778883453/},
  doi = {10.1007/3-540-62927-0_5},
  keywords = {ilp; logic}
}
 
Robert P. Nix. Editing by example. ACM Transactions on Programming Languages and Systems, 7(4):600-621, 1985.
@article{nix:1985,
  author = {Robert P. Nix},
  title = {Editing by example},
  journal = {{ACM} Transactions on Programming Languages and Systems},
  year = 1985,
  volume = 7,
  number = 4,
  pages = {600--621},
  address = {New York, NY, USA},
  url = {http://doi.acm.org/10.1145/4472.4476},
  issn = {0164-0925},
  keywords = {1985; Nix; article; editing by example; gap pattern},
  publisher = {{ACM}}
}
 
Roland J. Olsson and D. M. W. Powers. Machine learning of human language through automatic programming. In ICCS'03: Proceedings of the 4th International Conference on Cognitive Science (Sydney, Australia, July13-17, 2003), pages 507-512, 2003. Together with 7th ASCS Australasian Society for Cognitive Science Conference.
@inproceedings{olsson/powers:2003,
  author = {Roland J. Olsson and D. M. W. Powers},
  title = {Machine Learning of Human Language through Automatic
		  Programming},
  booktitle = {{ICCS'03}: Proceedings of the 4th International Conference
		  on Cognitive Science (Sydney, Australia, July\,13--17,
		  2003)},
  year = 2003,
  pages = {507--512},
  note = {Together with 7th ASCS Australasian Society for Cognitive
		  Science Conference},
  documenturl = {http://www-ia.hiof.no/~rolando/200302-ICCS-NLADATE.pdf},
  keywords = {adate; enumerative ip; functional programming; ifp;
		  induction; inductive programming; nlp; olsson; program
		  synthesis; program transformation}
}
 
Ronald J. Olsson and Brock Wilcox. Self-improvement for the ADATE automatic programming system. In GECCO'02: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation (New York, USA, July09-13, 2002), pages 893-897, San Francisco, CA, USA, 2002. Morgan Kaufmann.
@inproceedings{olsson/wilcox:2002,
  author = {Ronald J. Olsson and Brock Wilcox},
  title = {Self-improvement For The {ADATE} Automatic Programming
		  System},
  booktitle = {{GECCO'02}: Proceedings of the 4th Annual Conference on
		  Genetic and Evolutionary Computation (New York, USA,
		  July\,09--13, 2002)},
  year = 2002,
  pages = {893--897},
  address = {San Francisco, CA, USA},
  publisher = {Morgan Kaufmann},
  url = {http://portal.acm.org/citation.cfm?id=646205.683108&jmp=cit&coll=GUIDE&dl=GUIDE&CFID=98448670&CFTOKEN=49827412#CIT},
  isbn = {1-55860-878-8}
}
 
Roland J. Olsson. Inductive functional programming using incremental program transformation and Execution of logic programs by iterative-deepening A* SLD-tree search. Dr scient thesis, University of Oslo, Norway, 1994. Research report 189.
@phdthesis{olsson:1994,
  author = {Olsson, Roland J.},
  title = {Inductive functional programming using incremental program
		  transformation and Execution of logic programs by
		  iterative-deepening {A}* {SLD}-tree search},
  school = {University of Oslo},
  year = 1994,
  type = {Dr scient thesis},
  address = {Norway},
  note = {Research report 189},
  isbn = {82-7368-099-1},
  size = {156 pages}
}
 
Roland J. Olsson. Inductive functional programming using incremental program transformation. Artificial Intelligence, 74(1):55-83, 1995.
@article{olsson:1995,
  author = {Olsson, Roland J.},
  title = {Inductive Functional Programming using Incremental Program
		  Transformation},
  journal = {Artificial Intelligence},
  year = 1995,
  volume = 74,
  number = 1,
  pages = {55--83},
  keywords = {adate; enumerative ip; ifp; induction; inductive
		  programming; program synthesis}
}
 
Roland J. Olsson. The art of writing specifications for the ADATEautomatic programming system. In John R. Koza, Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors, GP'98: Proceedings of the Third Annual Conference (Madison, Wisconsin USA, July22-25, 1998), pages 278-283. Morgan Kaufmann, 1998.
@inproceedings{olsson:1998,
  author = {Olsson, Roland J.},
  title = {The Art of Writing Specifications for the {ADATE}Automatic
		  Programming System},
  editor = {John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla
		  and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and
		  Max H. Garzon and David E. Goldberg and Hitoshi Iba and
		  Rick Riolo},
  booktitle = {{GP'98}: Proceedings of the Third Annual Conference
		  (Madison, Wisconsin USA, July\,22--25, 1998)},
  year = 1998,
  pages = {278--283},
  publisher = {Morgan Kaufmann},
  isbn = {1-55860-548-7},
  documenturl = {http://www-ia.hiof.no/~rolando/specart5.ps},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.1763}
}
 
Roland J. Olsson. Population management for automatic design of algorithms through evolution. In Proceedings of the 1998 IEEE World Congress on Computational Intelligence, pages 592-597, Anchorage, Alaska, USA, 5-9 1998. IEEE Press.
@inproceedings{olsson:1998b,
  author = {Olsson, Roland J.},
  title = {Population Management for Automatic Design of Algorithms
		  through Evolution},
  booktitle = {Proceedings of the 1998 {IEEE} World Congress on
		  Computational Intelligence},
  year = 1998,
  pages = {592--597},
  address = {Anchorage, Alaska, USA},
  month = {5-9},
  publisher = {IEEE Press},
  url = {http://citeseer.ist.psu.edu/olsson98population.html}
}
 
Roland J. Olsson. How to invent functions. In Riccardo Poli, Peter Nordin, William B. Langdon, and Terence C. Fogarty, editors, Genetic Programming. 2nd European Workshop, EuroGP'99, Göteborg, Sweden, May26-27, 1999. Proceedings, volume 1598 of Lecture Notes in Computer Science, pages 232-243, Berlin/Heidelberg, 1999. Springer.
@inproceedings{olsson:1999,
  author = {Olsson, Roland J.},
  title = {How to Invent Functions},
  editor = {Riccardo Poli and Peter Nordin and William B. Langdon and
		  Terence C. Fogarty},
  booktitle = {Genetic Programming. 2nd European Workshop, {EuroGP'99},
		  G\"oteborg, Sweden, May\,26--27, 1999. Proceedings},
  year = 1999,
  series = {Lecture Notes in Computer Science},
  volume = 1598,
  pages = {232--243},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-65899-3},
  url = {http://www.springerlink.com/content/tbvm8wlqcww5tlg7/},
  abstract = {The paper presents the abstraction transformation which is
		  a fundamental method for creating functions in ADATE. The
		  use of abstraction turns out to be similar to evolution by
		  gene duplication which is emerging as the most important
		  theory of building blocks in natural genomes. We discuss
		  the relationship between abstraction and its natural
		  counterparts, but also give novel technical details on
		  automatic invention of functions. Basically, abstraction is
		  the reverse of the inlining transformation performed by
		  optimizing compilers.},
  doi = {10.1007/3-540-48885-5_20},
  documenturl = {http://alife.ccp14.ac.uk/adate/~rolando/abstrart1.ps},
  keywords = {adate; enumerative ip; functional programming; ifp;
		  induction; inductive programming; olsson; program
		  synthesis; program transformation}
}
 
Roland J. Olsson. Automatic design of algorithms through evolution (ADATE). In Emanuel Kitzelmann and Ute Schmid, editors, AAIP'07: Proceedings of the 2nd Workshop on Approaches and Applications of Inductive Programming (Warsaw, Poland, September17, 2007), page 1, 2007. Invited Talk.
@inproceedings{olsson:2007,
  author = {Roland J. Olsson},
  title = {Automatic Design of Algorithms through Evolution
		  ({ADATE})},
  editor = {Emanuel Kitzelmann and Ute Schmid},
  booktitle = {{AAIP'07}: Proceedings of the 2nd Workshop on Approaches
		  and Applications of Inductive Programming (Warsaw, Poland,
		  September\,17, 2007)},
  year = 2007,
  pages = 1,
  note = {Invited Talk},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip07/aaip_print.pdf},
  url = {http://www.cogsys.wiai.uni-bamberg.de/aaip07/}
}
 
Bjarte M. Ø stvold. Inductive synthesis of recursive functional programs (poster abstract). In ICFP'97: Proceedings of the 2nd ACM SIGPLAN international conference on Functional programming (Amsterdam, The Netherlands, June9-11, 1997), page 323, New York, NY, USA, 1997. ACM.
@inproceedings{ostvold:1997,
  author = {\O stvold, Bjarte M.},
  title = {Inductive synthesis of recursive functional programs
		  (poster abstract)},
  booktitle = {{ICFP'97}: Proceedings of the 2nd {ACM} {SIGPLAN}
		  international conference on Functional programming
		  (Amsterdam, The Netherlands, June\,9--11, 1997)},
  year = 1997,
  pages = 323,
  address = {New York, NY, USA},
  publisher = {{ACM}},
  url = {http://doi.acm.org/10.1145/258948.258992},
  documenturl = {http://portal.acm.org/ft_gateway.cfm?id=258992&type=pdf&coll=GUIDE&dl=GUIDE&CFID=96278412&CFTOKEN=67560760},
  isbn = {0-89791-918-1}
}
 
Derek Partridge, editor. Artificial Intelligence and Software Engineering. Ablex, Norwood, NJ, 1991.
@book{partridge:1991,
  editor = {Derek Partridge},
  title = {Artificial Intelligence and Software Engineering},
  publisher = {Ablex},
  year = 1991,
  address = {Norwood, NJ}
}
 
Derek Partridge. The case for inductive programming. Computer, 30(1):36-41, 1997.
@article{partridge:1997,
  author = {Derek Partridge},
  title = {The Case for Inductive Programming},
  journal = {Computer},
  year = 1997,
  volume = 30,
  number = 1,
  pages = {36--41},
  address = {Los Alamitos, CA, USA},
  publisher = {IEEE Computer Society},
  keywords = {inductive programming},
  url = {http://doi.ieeecomputersociety.org/10.1109/2.562924},
  abstract = {The science of creating software is based on deductive
		  methods. But induction, deduction's ignored sibling, could
		  have a profound effect on the future development of
		  computer science theory and practice. Computer scientists
		  and software developers in the late 1960s started a formal
		  science to guide software production. The underlying
		  framework of this science has always been based on
		  deduction (reasoning from the general to the specific)
		  rather than induction (reasoning from the specific to the
		  general). Today inductive programming is found only in
		  "machine learning," a subset of artificial intelligence.
		  Computer scientists may use inductive techniques to explore
		  a philosophy of cognition, develop a theory of adaptive
		  behavior, or find a way around a particularly awkward
		  problem, but they do not use it to create programs. Nearly
		  all basic computing science textbooks fail to include
		  inductive programming. However, inductive reasoning can
		  solve problems outside the realm of machine learning, too.
		  Formal methods to underpin inductive techniques are
		  emerging, but they have yet to be viewed, accepted, and
		  developed as a fundamental alternative to deductive
		  computer science.}
}
 
A. Passerini, P. Frasconi, and L. De Raedt. Kernels on prolog proof trees: Statistical learning in the ilp setting. In Emanuel Kitzelmann, Roland J. Olsson, and Ute Schmid, editors, AAIP'05: Proceedings of the 1st Workshop on Approaches and Applications of Inductive Programming (Bonn, Germany, Aug.7, 2005), pages 37-48, 2005. Full Paper.
@inproceedings{passerini_ea:2005,
  author = {A. Passerini and P. Frasconi and L. De~Raedt},
  title = {Kernels on Prolog Proof Trees: Statistical Learning in the
		  ILP Setting},
  editor = {Emanuel Kitzelmann and Roland J. Olsson and Ute Schmid},
  booktitle = {{AAIP'05}: Proceedings of the 1st Workshop on Approaches
		  and Applications of Inductive Programming (Bonn, Germany,
		  Aug.\,7, 2005)},
  year = 2005,
  pages = {37--48},
  note = {Full Paper},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip05/proceedings/paper.pdf}
}
 
Dusko Pavlovic and Douglas R. Smith. Software development by refinement. In Formal Methods at the Crossroads: From Panacea to Foundational Support. 10th Anniversary Colloquium of UNU/IIST the International Institute for Software Technology of The United Nations University Lisbon, Portugal, March18-20, 2002. Revised Papers, volume 2757 of Lecture Notes in Computer Science, pages 267-286, Berlin/Heidelberg, 2003. Springer.
@inproceedings{pavlovic/smith:2003,
  author = {Pavlovic, Dusko and Smith, Douglas R.},
  title = {Software Development by Refinement},
  booktitle = { Formal Methods at the Crossroads: From Panacea to
		  Foundational Support. 10th Anniversary Colloquium of
		  {UNU/IIST} the International Institute for Software
		  Technology of The United Nations University Lisbon,
		  Portugal, March\,18-20, 2002. Revised Papers},
  year = 2003,
  series = {Lecture Notes in Computer Science},
  volume = 2757,
  pages = {267--286},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-20527-2},
  url = {http://www.springerlink.com/content/tmf8f6tgfyvgdrvp},
  doi = {10.1007/978-3-540-40007-3_17},
  keywords = {ase; deductive program synthesis; inproceedings; kestrel;
		  overview; program synthesis; software engineering},
  abstract = {This paper presents an overview of the technical
		  foundations and current directions of Kestrel’s
		  approach to mechanizing software development. The approach
		  emphasizes machine-supported refinement of
		  property-oriented specifications to code, based on a
		  category of higher-order specifications. A key idea is
		  representing knowledge about programming concepts, such as
		  algorithm design, and datatype refinement by means of
		  taxonomies of design theories and refinements. Concrete
		  refinements are generated by composing library refinements
		  with a specification. The framework is partially
		  implemented in the research systems Specware, Designware,
		  Epoxi, and Planware. Specware provides basic support for
		  composing specifications and refinements via colimit, and
		  for generating code via logic morphisms. Specware is
		  intended to be general-purpose and has found use in
		  industrial settings. Designware extends Specware with
		  taxonomies of software design theories and support for
		  constructing refinements from them. Epoxi builds on
		  Designware to support the specification and refinement of
		  systems. Planware transforms behavioral models of tasks and
		  resources into high-performance scheduling algorithms. A
		  few applications of these systems are presented. ER -}
}
 
Nancy Pennington. Cognitive components of expertise in computer programming: A review of the literature (Technical report of the Graduate School of Business, University of Chicago). University of Chicago, Center for Decision Research, Chicago, 1982.
@book{pennington:1982,
  author = {Nancy Pennington},
  title = {Cognitive components of expertise in computer programming:
		  A review of the literature (Technical report of the
		  Graduate School of Business, University of Chicago)},
  publisher = {University of Chicago, Center for Decision Research},
  year = 1982,
  address = {Chicago}
}
 
P. L. Pirolli and John R. Anderson. The role of learning from examples in the acquisition of recursive programming skills. Canadian Journal of Psychology, 39:240-272, 1985.
@article{pirolli/anderson:1985,
  author = {P. L. Pirolli and John R. Anderson},
  title = {The role of learning from examples in the acquisition of
		  recursive programming skills},
  journal = {Canadian Journal of Psychology},
  year = 1985,
  volume = 39,
  pages = {240--272},
  annote = {ute-psylit}
}
 
P. L. Pirolli. A cognitive model and computer tutor for programming recursion. Human-Computer-Interaction, 2:319-355, 1986.
@article{pirolli:1986,
  author = {P. L. Pirolli},
  title = {A cognitive model and computer tutor for programming
		  recursion},
  journal = {Human-Computer-Interaction},
  year = 1986,
  volume = 2,
  pages = {319--355},
  annote = {ute-psylit}
}
 
G. D. Plotkin. A note on inductive generalization. In B. Meltzer and D. Michie, editors, Machine Intelligence, volume 5, pages 153-163. Edinburgh University Press, Edinburgh, 1969.
@incollection{plotkin:1969,
  author = {G. D. Plotkin},
  title = {A Note on Inductive Generalization},
  editor = {B. Meltzer and D. Michie},
  booktitle = {Machine Intelligence},
  publisher = {Edinburgh University Press},
  year = 1969,
  volume = 5,
  pages = {153--163},
  address = {Edinburgh}
}
 
G. D. Plotkin. A further note on inductive generalization. In Machine Intelligence, volume 6, pages 101-124. Edinburgh University Press, 1971.
@incollection{plotkin:1971,
  author = {G. D. Plotkin},
  title = {A further note on inductive generalization},
  booktitle = {Machine Intelligence},
  publisher = {Edinburgh University Press},
  year = 1971,
  volume = 6,
  pages = {101--124},
  keywords = {1971; Golem; ILP; Plotkin; inductive logic programming;
		  relative least general generalization; rlgg}
}
 
G. D. Plotkin. Automatic Methods of Inductive Inference. PhD thesis, Edinburgh University, 1971.
@phdthesis{plotkin:1971b,
  author = {G. D. Plotkin},
  title = {Automatic Methods of Inductive Inference},
  school = {Edinburgh University},
  year = 1971,
  keywords = {ILP; PhD; Plotkin; inductive logic programming; relative
		  least general generalization; rlgg}
}
 
J. Ross Quinlan and R. Mike Cameron-Jones. FOIL: A midterm report. In Pavel Brazdil, editor, Machine Learning: ECML-93. European Conference on Machine Learning, Vienna, Austria, April5-7, 1993. Proceedings, volume 667 of Lecture Notes in Computer Science, pages 1-20, Berlin/Heidelberg, 1993. Springer.
@inproceedings{quinlan/cameron-jones:1993,
  author = {J. Ross Quinlan and R. Mike Cameron-Jones},
  title = {{FOIL}: {A} Midterm Report},
  editor = {Pavel Brazdil},
  booktitle = {Machine Learning: {ECML-93}. European Conference on
		  Machine Learning, Vienna, Austria, April\,5--7, 1993.
		  Proceedings},
  year = 1993,
  series = {Lecture Notes in Computer Science},
  volume = 667,
  pages = {1--20},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-56602-1},
  url = {http://www.springerlink.com/content/f912432307714257/},
  abstract = {FOIL is a learning system that constructs Horn clause
		  programs from examples. This paper summarises the
		  development of FOIL from 1989 up to early 1993 and
		  evaluates its effectiveness on a non-trivial sequence of
		  learning tasks taken from a Prolog programming text.
		  Although many of these tasks are handled reasonably well,
		  the experiment highlights some weaknesses of the current
		  implementation. Areas for further research are
		  identified.},
  doi = {10.1007/3-540-56602-3_124},
  documenturl = {http://www.rulequest.com/Personal/q+cj.ecml93.ps}
}
 
J. Ross Quinlan and R. Mike Cameron-Jones. Induction of logic programs: FOIL and related systems. New Generation Computing, 13(3&4):287-312, 1995.
@article{quinlan/cameron-jones:1995,
  author = {J. Ross Quinlan and R. Mike Cameron-Jones},
  title = {Induction of Logic Programs: {FOIL} and Related Systems},
  journal = {New Generation Computing},
  year = 1995,
  volume = 13,
  number = {3\&4},
  pages = {287--312},
  annote = {foil, particularly dealing with closed worlds and making
		  clauses more understandable},
  keywords = {FOIL; ILP; inductive logic programming; inductive
		  programming},
  url = {http://dblp.uni-trier.de/db/journals/ngc/ngc13.html#QuinlanC95}
}
 
J. Ross Quinlan. Induction of decision trees. Machine Learning, 1(1):81-106, March 1986.
@article{quinlan:1986,
  author = {J. Ross Quinlan},
  title = {Induction of Decision Trees},
  journal = {Machine Learning},
  year = 1986,
  volume = 1,
  number = 1,
  pages = {81--106},
  month = {March},
  publisher = {Springer},
  address = {Netherlands},
  issn = {0885-6125 (Print) 1573-0565 (Online)},
  url = {http://www.springerlink.com/content/q30185t13m5n7000/},
  abstract = {The technology for building knowledge-based systems by
		  inductive inference from examples has been demonstrated
		  successfully in several practical applications. This paper
		  summarizes an approach to synthesizing decision trees that
		  has been used in a variety of systems, and it describes one
		  such system, ID3, in detail. Results from recent studies
		  show ways in which the methodology can be modified to deal
		  with information that is noisy and/or incomplete. A
		  reported shortcoming of the basic algorithm is discussed
		  and two means of overcoming it are compared. The paper
		  concludes with illustrations of current research
		  directions.},
  doi = {10.1023/A:1022643204877},
  keywords = {classification; induction; decision trees; information
		  theory; knowledge acquisition; expert systems}
}
 
J. Ross Quinlan. Learning logical definitions from relations. Machine Learning, 5(3):239-266, August 1990. Original foil paper.
@article{quinlan:1990,
  author = {J. Ross Quinlan},
  title = {Learning Logical Definitions from Relations},
  journal = {Machine Learning},
  year = 1990,
  volume = 5,
  number = 3,
  pages = {239--266},
  month = {August},
  note = {Original foil paper},
  publisher = {Springer},
  address = {Netherlands},
  issn = {0885-6125 (Print) 1573-0565 (Online)},
  url = {http://www.springerlink.com/content/r7155207778n7730/},
  abstract = {This paper describes FOIL, a system that learns Horn
		  clauses from data expressed as relations. FOIL is based on
		  ideas that have proved effective in attribute-value
		  learning systems, but extends them to a first-order
		  formalism. This new system has been applied successfully to
		  several tasks taken from the machine learning literature.},
  doi = {10.1023/A:1022699322624},
  keywords = {FOIL; ILP; Induction; empirical learning; first order
		  rules; inductive logic programming; inductive programming;
		  relational data; }
}
 
J. Ross Quinlan. Determinate literals in inductive logic programming. In John Mylopoulos and Raymond Reiter, editors, IJCAI'91: Proceedings of the 12th International Joint Conference on Artificial Intelligence (Sydney, Australia, Aug.24-30, 1991). Morgan Kaufmann, 1991.
@inproceedings{quinlan:1991,
  author = {J. Ross Quinlan},
  title = {Determinate Literals in Inductive Logic Programming},
  editor = {Mylopoulos, John and Reiter, Raymond},
  booktitle = {{IJCAI}'91: Proceedings of the 12th International Joint
		  Conference on Artificial Intelligence (Sydney, Australia,
		  Aug.\,24--30, 1991)},
  year = 1991,
  publisher = {Morgan Kaufmann},
  isbn = {1-55860-160-0},
  documenturl = {http://dli.iiit.ac.in/ijcai/IJCAI-91-VOL2/PDF/021.pdf},
  keywords = {applications; enumerative ip; foil; ilp; induction;
		  inductive programming; inproceedings; machine learning;
		  program synthesis},
  abstract = {A recent system, FOIL, constructs Horn programs from
		  numerous examples. Computational efficiency is achieved by
		  using greedy search guided by an information-based
		  heuristic. Greedy search tends to be myopic but determinate
		  terms, an adaptation of an idea introduced by another new
		  system (GOLEM), has been found to provide many of the
		  benefits of lookahead without substantial increases in
		  computation. This paper sketches key ideas from FOIL and
		  GOLEM and discusses the use of determinate literals in a
		  greedy search context. The efficacy of this approach is
		  illustrated on the task of learning the quicksort procedure
		  and other small but non-trivial list-manipulation
		  functions. }
}
 
J. Ross Quinlan. Boosting first-order learning. In Setsuo Arikawa and Arun Sharma, editors, Algorithmic Learning Theory. 7th International Workshop, ALT'96, Sydney, Australia, Oct.23-25, 1996. Proceedings, volume 1160 of Lecture Notes in Computer Science, pages 143-155, Berlin/Heidelberg, 1996. Springer.
@inproceedings{quinlan:1996,
  author = {J. Ross Quinlan},
  title = {Boosting First-Order Learning},
  editor = {Setsuo Arikawa and Arun Sharma},
  booktitle = {Algorithmic Learning Theory. 7th International Workshop,
		  {ALT'96}, Sydney, Australia, Oct.\,23--25, 1996.
		  Proceedings},
  year = 1996,
  series = {Lecture Notes in Computer Science},
  volume = 1160,
  pages = {143--155},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  keywords = {1996; Boosting; FFOIL; ILP; Quinlan; inductive logic
		  programming; },
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-61863-8},
  url = {http://www.springerlink.com/content/yw27157006846372/},
  abstract = {Several empirical studies have confirmed that boosting
		  classifier-learning systems can lead to substantial
		  improvements in predictive accuracy. This paper reports
		  early experimental results from applying boosting toffoil,
		  a first-order system that constructs definitions of
		  functional relations. Although the evidence is less
		  convincing than that for propositional-level learning
		  systems, it suggests that boosting will also prove
		  beneficial for first-order induction.},
  doi = {10.1007/3-540-61863-5_42}
}
 
J. Ross Quinlan. Learning first-order definitions of functions. Journal of Artificial Intelligence Research, 5:139-161, 1996.
@article{quinlan:1996b,
  author = {J. Ross Quinlan},
  title = {Learning First-Order Definitions of Functions},
  journal = {Journal of Artificial Intelligence Research},
  year = 1996,
  volume = 5,
  pages = {139--161},
  editor = {Steven Minton},
  keywords = {applications; article; enumerative ip; foil; ilp;
		  induction; inductive programming; machine learning; program
		  synthesis},
  annote = {ffoil, tackles some problems of foil when learning
		  functional relations},
  abstract = {First-order learning involves finding a clause-form
		  definition of a relation from examples of the relation and
		  relevant background information. In this paper, a
		  particular first-order learning system is modified to
		  customize it for finding definitions of functional
		  relations. This restriction leads to faster learning times
		  and, in some cases, to definitions that have higher
		  predictive accuracy. Other first-order learning systems
		  might benefit from similar specialization.},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.5035},
  documenturl = {http://www.jair.org/media/308/live-308-1570-jair.pdf}
}
 
J. Ross Quinlan. Relational learning and boosting. In Saso Dzeroski and Nada Lavrac, editors, Relational Data Mining, chapter 12, pages 292-304. Springer, New York, NY, USA, 2001.
@incollection{quinlan:2001,
  author = {J. Ross Quinlan},
  title = {Relational learning and boosting},
  editor = {Saso Dzeroski and Nada Lavrac},
  booktitle = {Relational Data Mining},
  publisher = {Springer},
  year = 2001,
  chapter = 12,
  pages = {292--304},
  address = {New York, NY, USA},
  abstract = {Boosting, a methodology for constructing and combining
		  multiple classifiers, has been found to lead to substantial
		  improvements in predictive accuracy. Although boosting was
		  formulated in a propositional learning context, the same
		  ideas can be applied to first-order learning (also known as
		  inductive logic programming). Boosting is used here with a
		  system that learns relational definitions of functions.
		  Results show that the occasional negative impact of
		  boosting all resemble the corresponding observations for
		  propositional learning.},
  isbn = {3-540-42289-7},
  keywords = {FFOIL; FOIL; ILP; boosting; inductive logic programming;
		  inductive programming},
  url = {http://portal.acm.org/citation.cfm?id=567237#}
}
 
M. R. K. Krishna Rao. A class of Prolog programs inferable from positive data. In S. Arikawa and A. K. Sharma, editors, Algorithmic Learning Theory. 7th International Workshop, ALT'96, Sydney, Australia, Oct.23-25, 1996. Proceedings, volume 1160 of Lecture Notes in Computer Science, pages 272-284, Berlin/Heidelberg, 1996. Springer.
@inproceedings{rao:1996,
  author = {M. R. K. Krishna Rao},
  title = {A class of {Prolog} programs inferable from positive
		  data},
  editor = {S. Arikawa and A. K. Sharma},
  booktitle = {Algorithmic Learning Theory. 7th International Workshop,
		  {ALT'96}, Sydney, Australia, Oct.\,23--25, 1996.
		  Proceedings},
  year = 1996,
  series = {Lecture Notes in Computer Science},
  volume = 1160,
  pages = {272--284},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-61863-8},
  url = {http://www.springerlink.com/content/p375410783158155/},
  abstract = {In this paper, we identify a class of Prolog programs
		  inferable from positive data. Our approach is based on
		  moding information and linear predicate inequalities
		  between input terms and output terms. Our results
		  generalize the results of Arimura and Shinohara [4].
		  Standard programs for reverse, quick-sort, merge-sort are a
		  few examples of programs that can be handled by our results
		  but not by the earlier results of [4]. The generality of
		  our results follows from the fact that we treat logical
		  variables as transmitters for broadcasting communication,
		  whereas Arimura and Shinohara [4] treat them as
		  point-to-point communication channels.},
  doi = {10.1007/3-540-61863-5_52},
  annote = {ute-inflit}
}
 
M. R. K. Krishna Rao. A framework for incremental learning of logic programs. Theoretical Computer Science, 185(1):191-213, 1997.
@article{rao:1997,
  author = {M. R. K. Krishna Rao},
  title = {A framework for incremental learning of logic programs},
  journal = {Theoretical Computer Science},
  year = 1997,
  volume = 185,
  number = 1,
  pages = {191--213},
  publisher = {Elsevier Science Publishers Ltd.},
  address = {Essex, UK},
  url = {http://dx.doi.org/10.1016/S0304-3975(97)00021-2}
}
 
M. R. K. Krishna Rao. Some classes of prolog programs inferable from positive data. Theoretical Computer Science, 241(1-2):211-234, 2000. Special issue for ALT'96.
@article{rao:2000,
  author = {M. R. K. Krishna Rao},
  title = {Some classes of Prolog programs inferable from positive
		  data},
  journal = {Theoretical Computer Science},
  year = 2000,
  volume = 241,
  number = {1-2},
  pages = {211--234},
  note = {Special issue for {ALT'96}}
}
 
M. R. K. Krishna Rao. Inductive inference of term rewriting systems from positive data. In Algorithmic Learning Theory. 15th International Conference, ALT'04, Padova, Italy, Oct.2-5, 2004. Proceedings, volume 3244 of Lecture Notes in Artificial Intelligence, pages 69-82, Berlin/Heidelberg, 2004. Springer.
@inproceedings{rao:2004,
  author = {M. R. K. Krishna Rao},
  title = {Inductive Inference of Term Rewriting Systems from
		  Positive Data},
  booktitle = {Algorithmic Learning Theory. 15th International
		  Conference, {ALT'04}, Padova, Italy, Oct.\,2--5, 2004.
		  Proceedings},
  year = 2004,
  series = {Lecture Notes in Artificial Intelligence},
  volume = 3244,
  pages = {69--82},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  url = {http://www.springerlink.com/content/da9c44u3ueljj227},
  doi = {10.1007/978-3-540-30215-5_7},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-23356-5},
  keywords = {inductive programming; learnability; program synthesis;
		  term rewriting},
  abstract = {In this paper, we study inferability of term rewriting
		  systems from positive examples alone. We define a class of
		  simple flat term rewriting systems that are inferable from
		  positive examples. In flat term rewriting systems, nesting
		  of defined symbols is forbidden in both left- and
		  right-hand sides. A flat TRS is simple if the size of
		  redexes in the right-hand sides is bounded by the size of
		  the corresponding left-hand sides. The class of simple flat
		  TRSs is rich enough to include many divide-and-conquer
		  programs like addition, doubling, tree-count, list-count,
		  split, append, etc. The relation between our results and
		  the known results on Prolog programs is also discussed. In
		  particular, flat TRSs can define functions (like doubling),
		  whose output is bigger in size than the input, which is not
		  possible with linearly-moded Prolog programs.}
}
 
M. R. K. Krishna Rao. A class of prolog programs with non-linear outputs inferable from positive data. In Algorithmic Learning Theory. 16th International Conference, ALT 2005, Singapore, October 8-11, 2005. Proceedings, volume 3734 of Lecture Notes in Computer Science, pages 312-326, Berlin/Heidelberg, 2005. Springer.
@inproceedings{rao:2005,
  author = {M. R. K. Krishna Rao},
  title = {A Class of Prolog Programs with Non-linear Outputs
		  Inferable from Positive Data},
  booktitle = {Algorithmic Learning Theory. 16th International
		  Conference, ALT 2005, Singapore, October 8-11, 2005.
		  Proceedings},
  year = 2005,
  series = {Lecture Notes in Computer Science},
  volume = 3734,
  pages = {312--326},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-29242-5},
  url = {http://www.springerlink.com/content/aav9qlrv8df5j3g1/},
  abstract = {In this paper, we study inferability of Prolog programs
		  from positive examples alone. We define a class of Prolog
		  programs called recursion bounded programs that can capture
		  non-linear relationships between inputs and outputs and yet
		  inferable from positive examples. This class is rich enough
		  to include many programs like append, delete, insert,
		  reverse, permute, count, listsum, listproduct,
		  insertion-sort, quick-sort on lists, various tree traversal
		  programs and addition, multiplication, factorial, power on
		  natural numbers. The relation between our results and the
		  known results is also discussed. In particular, the class
		  of recursion bounded programs contains all the known
		  terminating linearly-moded Prolog programs of Krishna Rao
		  [7] and additional programs like power on natural numbers
		  which do not belong to the class of linearly-moded programs
		  and the class of safe programs of Martin and Sharma [12].},
  doi = {10.1007/11564089_25}
}
 
M. R. K. Krishna Rao. Learnability of simply-moded logic programs from entailment. In Advances in Computer Science - ASIAN'04. Higher-Level Decision Making. 9th Asian Computing Science Conference. Dedicated to Jean-Louis Lassez on the Occasion of His 60th Birthday. Chiang Mai, Thailand, Dec.8-10, 2004. Proceedings, volume 3321 of Lecture Notes in Computer Science, pages 128-141, Berlin/Heidelberg, 2005. Springer.
@inproceedings{rao:2005b,
  author = {M. R. K. Krishna Rao},
  title = {Learnability of Simply-Moded Logic Programs from
		  Entailment},
  booktitle = {Advances in Computer Science -- {ASIAN'04}. Higher-Level
		  Decision Making. 9th Asian Computing Science Conference.
		  Dedicated to Jean-Louis Lassez on the Occasion of His 60th
		  Birthday. Chiang Mai, Thailand, Dec.\,8--10, 2004.
		  Proceedings},
  year = 2005,
  series = {Lecture Notes in Computer Science},
  volume = 3321,
  pages = {128--141},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  url = {http://www.springerlink.com/content/ckanleyfx7ahm740},
  keywords = {ilp; inductive programming; learnability; program
		  synthesis; recursion},
  abstract = {In this paper, we study exact learning of logic programs
		  from entailment queries and present a polynomial time
		  algorithm to learn a rich class of logic programs that
		  allow local variables and include many standard programs
		  like addition, multiplication, exponentiation, member,
		  prefix, suffix, length, append, merge, split, delete,
		  insert, insertion-sort, quick-sort, merge-sort, preorder
		  and inorder traversal of binary trees, polynomial
		  recognition, derivatives, sum of a list of naturals. Our
		  algorithm asks at most polynomial number of queries and our
		  class is the largest of all the known classes of programs
		  learnable from entailment.},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-24087-7},
  doi = {10.1007/978-3-540-30502-6_9}
}
 
M. R. K. Krishna Rao. Learning recursive prolog programs with local variables from examples. In Emanuel Kitzelmann, Roland J. Olsson, and Ute Schmid, editors, AAIP'05: Proceedings of the 1st Workshop on Approaches and Applications of Inductive Programming (Bonn, Germany, Aug.7, 2005), pages 51-57, 2005. Full Paper.
@inproceedings{rao:2005c,
  author = {M. R. K. Krishna Rao},
  title = {Learning Recursive Prolog Programs with Local Variables
		  from Examples},
  editor = {Emanuel Kitzelmann and Roland J. Olsson and Ute Schmid},
  booktitle = {{AAIP'05}: Proceedings of the 1st Workshop on Approaches
		  and Applications of Inductive Programming (Bonn, Germany,
		  Aug.\,7, 2005)},
  year = 2005,
  pages = {51--57},
  note = {Full Paper},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip05/proceedings/aaip_krishna.pdf}
}
 
M. R. K. Krishna Rao. Learnability of term rewrite systems from positive examples. In CATS'06: Proceedings of the 12th Computing: The Australasian Theroy Symposium (Hobart, Australia, Jan.16-19, 2006), volume 51, pages 133-137. Australian Computer Society, Inc., 2006.
@inproceedings{rao:2006,
  author = {M. R. K. Krishna Rao},
  title = {Learnability of Term Rewrite Systems from Positive
		  Examples},
  booktitle = {{CATS'06}: Proceedings of the 12th Computing: The
		  Australasian Theroy Symposium (Hobart, Australia,
		  Jan.\,16--19, 2006)},
  year = 2006,
  volume = 51,
  pages = {133--137},
  publisher = {Australian Computer Society, Inc.},
  url = {http://portal.acm.org/citation.cfm?id=1151801},
  keywords = {inductive programming; learnability; program synthesis;
		  term rewriting},
  abstract = {Learning from examples is an important characteristic
		  feature of intelligence in both natural and artificial
		  intelligent agents. In this paper, we study learnability of
		  term rewriting systems from positive examples alone. We
		  define a class of linear-bounded term rewriting systems
		  that are inferable from positive examples. In
		  linear-bounded term rewriting systems, nesting of defined
		  symbols is allowed in right-hand sides, unlike the class of
		  flat systems considered in Krishna Rao [8]. The class of
		  linear-bounded TRSs is rich enough to include many
		  divide-and-conquer programs like addition, logarithm,
		  tree-count, list-count, split, append, reverse etc.}
}
 
M. R. K. Krishna Rao. Some classes of term rewriting systems inferable from positive data. Theoretical Computer Science, 397(1-3):129-149, 2008.
@article{rao:2008,
  author = {M. R. K. Krishna Rao},
  title = {Some Classes of Term Rewriting Systems Inferable from
		  Positive Data},
  journal = {Theoretical Computer Science},
  year = 2008,
  volume = 397,
  number = {1-3},
  pages = {129--149},
  address = {Essex, UK},
  publisher = {Elsevier Science Publishers Ltd.},
  url = {http://dx.doi.org/10.1016/j.tcs.2008.02.027},
  keywords = {inductive programming; learnability; program synthesis;
		  term rewriting},
  abstract = {In this paper, we study the inferability of term rewriting
		  systems (trss, for short) from positive examples alone. Two
		  classes of trss inferable from positive data are presented,
		  namely, simple flat trss and linear-bounded trss. These
		  classes of trss are rich enough to include many
		  divide-and-conquer programs like addition, doubling,
		  logarithm, tree-count, list-count, split, append, reverse,
		  etc. The classes of simple flat trss and linear-bounded
		  trss are incomparable, i.e., there are functions that can
		  be computed by simple flat trss but not by linear-bounded
		  trss and vice versa.}
}
 
J. C. Reynolds. Towards a theory of type structure. In Programming Symposium. Proceedings, Colloque sur la Programmation Paris, April9-11, 1974, volume 19 of Lecture Notes in Computer Science, pages 408-425, Berlin/Heidelberg, 1974. Springer.
@inproceedings{reynolds:1974,
  author = {J. C. Reynolds},
  title = {Towards a Theory of Type Structure},
  booktitle = {Programming Symposium. Proceedings, Colloque sur la
		  Programmation Paris, April\,9--11, 1974},
  year = 1974,
  series = {Lecture Notes in Computer Science},
  volume = 19,
  pages = {408--425},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-06859-4},
  url = {http://www.springerlink.com/content/p5801737k78207p7/},
  abstract = {Without Abstract},
  doi = {10.1007/3-540-06859-7_148},
  keywords = {lambda calculus; recursion theory; seminal paper; system
		  f},
  annote = {one of the two original works on system f}
}
 
C. Rich and Richard C. Waters. The programmer's apprentice: a session with KBEmacs. Number 11 in IEEE Transactions on Software Engineering. IEEE Press, November 1985.
@book{rich/waters:1985,
  author = {C. Rich and Richard C. Waters},
  title = {The programmer's apprentice: a session with {KBE}macs},
  publisher = {IEEE Press},
  year = 1985,
  series = {IEEE Transactions on Software Engineering},
  month = {November},
  pages = {1296--1320},
  number = 11,
  abstract = {The Knowledge-Based Editor in Emacs (KBEmacs) is the
		  current demonstration system implemented as part of the
		  Programmer's Apprentice project. KBEmacs is capable of
		  acting as a semiexpert assistant to a person who is writing
		  a program-taking over some parts of the programming task.
		  Using KBEmacs, it is possible to construct a program by
		  issuing a series of high level comnmands. This series of
		  commands can be as much as an order of magnitude shorter
		  than the program it describes.},
  doi = {http://doi.ieeecomputersociety.org/10.1109/TSE.1985.231880},
  keywords = {Programmer's Apprentice; Computer-aided design; program
		  editing; programming environments; reusable software
		  components}
}
 
C. Rich and Richard C. Waters. Automatic programming: Myths and prospects. IEEE Computer, 21(11):10-25, 1988.
@article{rich/waters:1988,
  author = {C. Rich and Richard C. Waters},
  title = {Automatic programming: {Myths} and prospects},
  journal = {IEEE Computer},
  year = 1988,
  volume = 21,
  number = 11,
  pages = {10--25}
}
 
Charles Rich and Richard C. Waters. Approaches to automatic programming. In M. C. Yovits, editor, Advances in Computers, volume 37. Academic Press, 1993.
@incollection{rich/waters:1993,
  author = {Charles Rich and Richard C. Waters},
  title = {Approaches to Automatic Programming},
  editor = {M. C. Yovits},
  booktitle = {Advances in Computers},
  publisher = {Academic Press},
  year = 1993,
  volume = 37
}
 
Riverson Rios and Stan Matwin. Efficient induction of recursive prolog definitions. In Advances in Artifical Intelligence. 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI'96 Toronto, Ontario, Canada, May21-24, 1996. Proceedings, volume 1081 of Lecture Notes in Computer Science, pages 240-248, Berlin/Heidelberg, 1996. Springer.
@inproceedings{rios/matwin:1996,
  author = {Riverson Rios and Stan Matwin},
  title = {Efficient Induction of Recursive Prolog Definitions},
  booktitle = {Advances in Artifical Intelligence. 11th Biennial
		  Conference of the Canadian Society for Computational
		  Studies of Intelligence, {AI'96} Toronto, Ontario, Canada,
		  May\,21--24, 1996. Proceedings},
  year = 1996,
  series = {Lecture Notes in Computer Science},
  volume = 1081,
  pages = {240--248},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-61291-9},
  url = {http://www.springerlink.com/content/78q08572p5m82450/},
  doi = {10.1007/3-540-61291-2_55},
  keywords = {CLAM; ilp; inductive programming; ip-system; program
		  synthesis; recursion},
  abstract = {The ability to learn recursive definitions is a desirable
		  characteristic of a learner. This paper presents Clam, a
		  system that efficiently learns Prolog purely and
		  left-recursive definitions from small data sets by using
		  inverse implication. A learning curve for Clam shows that
		  the accuracy grows with the increase of both positive and
		  negative examples. We believe our system can be used as a
		  preprocessor for a general-purpose system when few examples
		  are at hand.}
}
 
E. S. Roberts. Thinking recursively. Wiley, New York, 1986.
@book{roberts:1986,
  author = {E. S. Roberts},
  title = {Thinking recursively},
  publisher = {Wiley},
  year = 1986,
  address = {New York}
}
 
Raúl Rojas. Neural Networks - A Systematic Introduction. Springer, 1996.
@book{rojas:1996,
  author = {Ra\'{u}l Rojas},
  title = {Neural Networks -- A Systematic Introduction},
  publisher = {Springer},
  year = 1996,
  keywords = {neuralnetworks}
}
 
Paul S. Rosenbloom and Alan Newell. The chunking of goal hierarchies: A generalized model of practice. In Ryszard S. Michalski, Jaime G. Carbonell, and Tom M. Mitchell, editors, Machine Learning. An Artificial Intelligence Approach, volume 2, chapter 10, pages 247-288. Morgan Kaufmann, Los Altos, CA, 1986.
@incollection{rosenbloom/newell:1986,
  author = {Paul S. Rosenbloom and Alan Newell},
  title = {The Chunking of Goal Hierarchies: A Generalized Model of
		  Practice},
  editor = {Ryszard S. Michalski and Jaime G. Carbonell and Tom M.
		  Mitchell},
  booktitle = {Machine Learning. An Artificial Intelligence Approach},
  publisher = {Morgan Kaufmann},
  year = 1986,
  volume = 2,
  chapter = 10,
  pages = {247--288},
  address = {Los Altos, CA},
  keywords = {cognition}
}
 
Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 3 edition, 2010.
@book{russell/norvig:2010,
  author = {Stuart Russell and Peter Norvig},
  title = {Artificial Intelligence: A Modern Approach},
  publisher = {Prentice Hall},
  year = 2010,
  edition = 3,
  keywords = {ai}
}
 
Ken Sadohara and Makoto Haraguchi. Analogical logic program synthesis from examples. In Nada Lavrac and Stefan Wrobel, editors, Machine Learning: ECML-95. 8th European Conference on Machine Learning Heraclion, Crete, Greece, April25-27, 1995. Proceedings, volume 912 of Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, pages 232-244, Berlin/Heidelberg, 1995. Springer.
@inproceedings{sadohara/haraguchi:1995,
  author = {Ken Sadohara and Makoto Haraguchi},
  title = {Analogical logic program synthesis from examples},
  editor = {Nada Lavrac and Stefan Wrobel},
  booktitle = {Machine Learning: {ECML-95}. 8th European Conference on
		  Machine Learning Heraclion, Crete, Greece, April\,25--27,
		  1995. Proceedings},
  year = 1995,
  series = {Lecture Notes in Computer Science. Lecture Notes in
		  Artificial Intelligence},
  volume = 912,
  pages = {232--244},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-59286-0},
  url = {http://www.springerlink.com/content/7881314375u15320/},
  abstract = {The purpose of this paper is to present a theory and an
		  algorithm for analogical logic program synthesis from
		  examples. Given a source program and examples, the task of
		  our algorithm is to find a program which explains the
		  examples correctly and is similar to the source program.
		  Although we can define a notion of similarity in various
		  ways, we consider a class of similarities from the
		  viewpoint of how examples are explained by a program. In a
		  word, two programs are said to be similar if they share a
		  common explanation structure at an abstract level. Using
		  this notion of similarity, we formalize an analogical logic
		  program synthesis and show that our algorithm based on a
		  framework of model inference can identify a desired
		  program.},
  doi = {10.1007/3-540-59286-5_61},
  annote = {ute-inflit}
}
 
Yasubumi Sakakibara. Learning context-free grammars from structural data in polynomial time. Theoretical Computer Science, 76(2-3):223-242, November 1990.
@article{sakakibara:1990,
  author = {Yasubumi Sakakibara},
  title = {Learning Context-free Grammars from Structural Data in
		  Polynomial Time},
  journal = {Theoretical Computer Science},
  year = 1990,
  volume = 76,
  number = {2--3},
  pages = {223--242},
  month = {November},
  publisher = {Elsevier Science Publishers Ltd.},
  address = {Essex, UK},
  doi = {10.1016/0304-3975(90)90017-C},
  annote = {ute-inflit}
}
 
Yasubumi Sakakibara. Efficient learning of context-free grammars from positive structural examples. Information and Computation, 97:23-60, 1992.
@article{sakakibara:1992,
  author = {Yasubumi Sakakibara},
  title = {Efficient Learning of Context-Free Grammars from Positive
		  Structural Examples},
  journal = {Information and Computation},
  year = 1992,
  volume = 97,
  pages = {23--60},
  annote = {ute-inflit}
}
 
Yasubumi Sakakibara. Recent advances of grammatical inference. Theoretical Computer Science, 185(1):15-45, October 1997.
@article{sakakibara:1997,
  author = {Yasubumi Sakakibara},
  title = {Recent Advances of Grammatical Inference},
  journal = {Theoretical Computer Science},
  year = 1997,
  volume = 185,
  number = 1,
  pages = {15--45},
  month = {October},
  publisher = {Elsevier Science Publishers Ltd.},
  address = {Essex, UK},
  doi = {10.1016/S0304-3975(97)00014-5},
  annote = {ute-inflit}
}
 
K. Schädler, U. Schmid, B. Machenschalk, and H. Lübben. A neural net for determining structural similarity of recursive programs. In R. Bergmann and W. Wilke, editors, GWCBR'97: Proceedings of the 5th German Workshop on Case-Based Reasoning. Foundations, Systems, and Applications (Bad Honnef, Germany, March4-5, 1997), pages 199-206, 1997. LSA-97-01E.
@inproceedings{schaedler_ea:1997,
  author = {Sch{\"a}dler, K. and Schmid, U. and Machenschalk, B. and
		  L{\"u}bben, H.},
  title = {A neural net for determining structural similarity of
		  recursive programs},
  editor = {Bergmann, R. and Wilke, W.},
  booktitle = {{GWCBR'97}: Proceedings of the 5th German Workshop on
		  Case-Based Reasoning. Foundations, Systems, and
		  Applications (Bad Honnef, Germany, March\,4--5, 1997)},
  year = 1997,
  pages = {199--206},
  note = {LSA-97-01E}
}
 
Ute Schmid and Roland J. Olsson, editors. Special Topic on Approaches and Applications of Inductive Programming, volume 7. MIT Press, 2006.
@book{schmid/olsson:2006,
  editor = {Ute Schmid and Roland J. Olsson},
  title = {Special Topic on Approaches and Applications of Inductive
		  Programming},
  publisher = {MIT Press},
  year = 2006,
  volume = 7,
  journal = {Journal of Machine Learning Research},
  url = {http://jmlr.csail.mit.edu/papers/topic/inductive_programming.html}
}
 
Ute Schmid and Jens Waltermann. Automatic synthesis of XSL-transformations from example documents. In M. H. Hamza, editor, AIA'04: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications Proceedings (Innsbruck, Austria, Febr.16-18, 2004), Artificial Intelligence and Soft Computing, pages 252-257, Anaheim, 2004. Acta Press.
@inproceedings{schmid/waltermann:2004,
  author = {Ute Schmid and Jens Waltermann},
  title = {Automatic Synthesis of {XSL}-Transformations from Example
		  Documents},
  editor = {Hamza, M. H.},
  booktitle = {{AIA'04}: Proceedings of the {IASTED} International
		  Conference on Artificial Intelligence and Applications
		  Proceedings (Innsbruck, Austria, Febr.\,16--18, 2004)},
  year = 2004,
  series = {Artificial Intelligence and Soft Computing},
  pages = {252--257},
  address = {Anaheim},
  publisher = {Acta Press}
}
 
Ute Schmid and Fritz Wysotzki. Induction of recursive program schemes. In Claire Nedellec and Céline Rouveirol, editors, Machine Learning: ECML-98. 10th European Conference on Machine Learning, Chemnitz, Germany, April21-23, 1998. Proceedings, volume 1398 of Lecture Notes in Computer Science, pages 214-225, Berlin/Heidelberg, 1998. Springer.
@inproceedings{schmid/wysotzki:1998,
  author = {Schmid, Ute and Wysotzki, Fritz},
  title = {Induction of Recursive Program Schemes},
  editor = {Claire Nedellec and C{\'e}line Rouveirol},
  booktitle = {Machine Learning: {ECML-98}. 10th European Conference on
		  Machine Learning, Chemnitz, Germany, April\,21--23, 1998.
		  Proceedings},
  year = 1998,
  series = {Lecture Notes in Computer Science},
  volume = 1398,
  pages = {214--225},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-64417-0},
  url = {http://www.springerlink.com/content/w607kk31573q1835/},
  abstract = {In this paper we present an approach to the induction of
		  recursive structures from examples which is based on the
		  notion of recursive program schemes. We separate induction
		  from examples in two stages: (1) constructing initial
		  programs from examples and (2) folding initial programs to
		  recursive program schemes. By this separation, the
		  induction of recursive program schemes can be reduced to a
		  pattern-matching problem which can be handled by a generic
		  algorithm. Construction of initial programs is performed
		  with an approach to universal planning.Background
		  knowledgeBackground knowledgeis given in the form of
		  operators and their conditions of application. Furthermore
		  synthesizing recursive program schemes instead of programs
		  in a predefined programming language enables us to combine
		  program synthesis and analogical reasoning. A recursive
		  program scheme represents the class of structural identical
		  programs and can be assigned different semantics by
		  interpretation. We believe that our approach mimicks in
		  some way the problem solving and learning behavior of a
		  (novice) human programmer and that our approach integrates
		  theoretical ideas and empirical results of learning by
		  doing and learning by analogy from cognitive science in a
		  unique framework.is given in the form of operators and
		  their conditions of application. Furthermore synthesizing
		  recursive program schemes instead of programs in a
		  predefined programming language enables us to combine
		  program synthesis and analogical reasoning. A recursive
		  program scheme represents the class of structural identical
		  programs and can be assigned different semantics by
		  interpretation. We believe that our approach mimicks in
		  some way the problem solving and learning behavior of a
		  (novice) human programmer and that our approach integrates
		  theoretical ideas and empirical results of learning by
		  doing and learning by analogy from cognitive science in a
		  unique framework.},
  keywords = {Inductive Program Synthesis; Planning and Learning;
		  Analogy; Cognitive Modelling},
  doi = {10.1007/BFb0026692}
}
 
Ute Schmid and Fritz Wysotzki. Skill acquisition can be regarded as program synthesis: An integrative approach to learning by doing and learning by analogy. In J. Krems, Ute Schmid, and Fritz Wysotzki, editors, Mind Modelling. A Cognitive Science Approach to Reasoning, Learning and Discovery, pages 253-284. Pabst Science Publishers, Lengerich, 1999.
@incollection{schmid/wysotzki:1999,
  author = {Ute Schmid and Fritz Wysotzki},
  title = {Skill acquisition can be regarded as program synthesis: An
		  integrative approach to learning by doing and learning by
		  analogy},
  editor = {J. Krems and Ute Schmid and Fritz Wysotzki},
  booktitle = {Mind Modelling. A Cognitive Science Approach to Reasoning,
		  Learning and Discovery},
  publisher = {Pabst Science Publishers},
  year = 1999,
  pages = {253--284},
  address = {Lengerich},
  url = {http://www.pabst-publishers.de/Psychologie/Buecher/buch99.htm}
}
 
Ute Schmid and Fritz Wysotzki. Applying inductive program synthesis to macro learning. In Steve Chien, Subbarao Kambhampati, and Craig A. Knoblock, editors, AIPS'00: Proceedings of the Fifth International Conference on Artificial Intelligence Planning Systems (Breckenridge, CO, USA, April14-17, 2000), pages 371-378, Menlo Park, CA, 2000. AAAI Press.
@inproceedings{schmid/wysotzki:2000,
  author = {Ute Schmid and Fritz Wysotzki},
  title = {Applying Inductive Program Synthesis to Macro Learning},
  editor = {Steve Chien and Subbarao Kambhampati and Craig A.
		  Knoblock},
  booktitle = {{AIPS'00}: Proceedings of the Fifth International
		  Conference on Artificial Intelligence Planning Systems
		  (Breckenridge, CO, USA, April\,14--17, 2000)},
  year = 2000,
  pages = {371--378},
  address = {Menlo Park, CA},
  publisher = {AAAI Press},
  isbn = {1-57735-111-8},
  keywords = {inductive programming; planning}
}
 
Ute Schmid and Fritz Wysotzki. A unifying approach to learning by doing and learning by analogy. In N. Callaos, editor, SCI'00: 4th World Multiconference on Systemics, Cybernetics and Informatics (Orlando, Florida, July23-26, 2000), volume 1, pages 379-384, Orlando, FL, 2000. International Institute of Informatics and Systemics.
@inproceedings{schmid/wysotzki:2000b,
  author = {Ute Schmid and Fritz Wysotzki},
  title = {A Unifying Approach to Learning by Doing and Learning by
		  Analogy},
  editor = {N. Callaos},
  booktitle = {{SCI'00}: 4th World Multiconference on Systemics,
		  Cybernetics and Informatics (Orlando, Florida,
		  July\,23--26, 2000)},
  year = 2000,
  volume = 1,
  pages = {379--384},
  address = {Orlando, FL},
  publisher = {International Institute of Informatics and Systemics},
  isbn = {980-07-6687-1}
}
 
Ute Schmid and Fritz Wysotzki. Applying Inductive Program Synthesis to Learning Domain-Dependent Control Knowledge - Transforming Plans into Programs. Technical Report CMU-CS-00-143, Computer Science Department, Carnegie Mellon University, Pittsburg, PA, 2000.
@techreport{schmid/wysotzki:2000c,
  author = {Ute Schmid and Fritz Wysotzki},
  title = {{Applying Inductive Program Synthesis to Learning
		  Domain-Dependent Control Knowledge -- Transforming Plans
		  into Programs}},
  institution = {Computer Science Department, Carnegie Mellon University},
  year = 2000,
  number = {CMU-CS-00-143},
  address = {Pittsburg, PA}
}
 
Ute Schmid and Fritz Wysotzki. Applying inductive programm synthesis to macro learning. In Steve Chien, Subbarao Kambhampati, and Craig A. Knoblock, editors, AIPS'00: Proceedings of the Fifth International Conference on Artificial Intelligence Planning Systems (Breckenridge, CO, USA, April14-17, 2000), pages 371-378, Menlo Park, CA, 2000. AAAI Press.
@inproceedings{schmid/wysotzki:2000d,
  author = {Ute Schmid and Fritz Wysotzki},
  title = {Applying Inductive Programm Synthesis to Macro Learning},
  editor = {Steve Chien and Subbarao Kambhampati and Craig A.
		  Knoblock},
  booktitle = {{AIPS'00}: Proceedings of the Fifth International
		  Conference on Artificial Intelligence Planning Systems
		  (Breckenridge, CO, USA, April\,14--17, 2000)},
  year = 2000,
  pages = {371--378},
  address = {Menlo Park, CA},
  publisher = {{AAAI Press}},
  isbn = {1-57735-111-8}
}
 
Ute Schmid. Adaptation of non-isomorphic sources in analogical problem solving. In K. Holyoak, D. Gentner, and B. Kokinov, editors, Proceedings of the Workshop Advances in Analogy Research: Integration of Theory and Data from the Cognitive, Computational, and Neural Sciences (Sofia, Bulgarian, July17-20, 1998), NBU Series in Cognitive Science, pages 406-407, Sofia, 1998. New Bulgarian University Press.
@inproceedings{schmid:1998,
  author = {Schmid, Ute},
  title = {Adaptation of non-isomorphic sources in analogical problem
		  solving},
  editor = {Holyoak, K. and Gentner, D. and Kokinov, B.},
  booktitle = {Proceedings of the Workshop Advances in Analogy Research:
		  Integration of Theory and Data from the Cognitive,
		  Computational, and Neural Sciences (Sofia, Bulgarian,
		  July\,17--20, 1998)},
  year = 1998,
  series = {NBU Series in Cognitive Science},
  pages = {406--407},
  address = {Sofia},
  publisher = {New Bulgarian University Press}
}
 
Ute Schmid. Analogical problem solving by adaptation of schemes. In F. E. Ritter and R. M. Young, editors, ECCM'98: Proceedings of the 2nd European Conference on Cognitive Modelling (Nottingham, UK, April1.-4, 1998). Nottingham University Press, 1998. Poster.
@inproceedings{schmid:1998b,
  author = {Ute Schmid},
  title = {Analogical Problem Solving by Adaptation of Schemes},
  editor = {F. E. Ritter and R. M. Young},
  booktitle = {{ECCM'98}: Proceedings of the 2nd European Conference on
		  Cognitive Modelling (Nottingham, UK, April\,1.--4, 1998)},
  year = 1998,
  publisher = {Nottingham University Press},
  note = {Poster},
  documenturl = {http://user.cs.tu-berlin.de/~schmid/pub-ps/eccm98-short.ps}
}
 
Ute Schmid. Iterative macro-operators revisited: Applying program synthesis to learning in planning. Technical Report CMU-CS-99-114, Computer Science Department, Carnegie Mellon University, Pittsburg, PA, 1999.
@techreport{schmid:1999,
  author = {Ute Schmid},
  title = {Iterative Macro-Operators Revisited: Applying Program
		  Synthesis to Learning in Planning},
  institution = {Computer Science Department, Carnegie Mellon University},
  year = 1999,
  number = {CMU-CS-99-114},
  address = {Pittsburg, PA}
}
 
Ute Schmid. Inductive Synthesis of Functional Programs. Universal Planning, Folding of Finite Programs, and Schema Abstraction by Analogical Reasoning, volume 2654 of Lecture Notes in Artificial Intelligence. Springer, Berlin/Heidelberg, 2003. Ute Schmid's habilitation thesis. May2001, Department of Electrical Engineering and Computer Science, TU Berlin.
@book{schmid:2003,
  author = {Ute Schmid},
  title = {Inductive Synthesis of Functional Programs. Universal
		  Planning, Folding of Finite Programs, and Schema
		  Abstraction by Analogical Reasoning},
  publisher = {Springer},
  year = 2003,
  volume = 2654,
  series = {Lecture Notes in Artificial Intelligence},
  address = {Berlin\,/\,Heidelberg},
  note = {Ute Schmid's habilitation thesis. May\,2001, Department of
		  Electrical Engineering and Computer Science, TU Berlin},
  keywords = {book; inductive programming},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-40174-2},
  url = {http://www.springerlink.com/content/mevxuj0c1q49/},
  doi = {10.1007/b12055},
  documenturl = {http://www.inf.uos.de/schmid/pub-ps/habil.ps.gz}
}
 
Ute Schmid. A Cognitive Model of Learning by Doing. Models And Human Reasoning, pages 235-252, 2005.
@article{schmid:2005,
  author = {Ute Schmid},
  title = {{A Cognitive Model of Learning by Doing}},
  journal = {Models And Human Reasoning},
  year = 2005,
  pages = {235--252},
  publisher = {Wissenschaft \& Technik Verlag},
  editor = {Bap, S. and Gulden, J. and Wieczorek, T.},
  address = {Berlin}
}
 
Ute Schmid, Martin Mühlpfordt, and Fritz Wysotzki. Induction of recursive program schemes as inference of context free tree grammers. Draft, 1998.
@unpublished{schmid_ea:1998,
  author = {Ute Schmid and Martin M{\"u}hlpfordt and Fritz Wysotzki},
  title = {Induction of Recursive Program Schemes as Inference of
		  Context Free Tree Grammers},
  year = 1998,
  note = {Draft},
  documenturl = {http://www.inf.uos.de/schmid/pub-ps/jml-article-draft.ps}
}
 
Ute Schmid, R. Mercy, and Fritz Wysotzki. Programming by analogy: Retrieval, mapping, adaptation and generalization of recursive program schemes. In FGML'98: Proceedings of the Annual Meeting of the GI Machine Learning Group (Technische Universität, Berlin, Aug.17.-19, 1998), volume 98 of Forschungsberichte des Fachbereichs Informatik, pages 140-147, TU Berlin, 1998. Beiträge zum Treffen der GI-Fachgruppe 1.1.3 Maschinelles Lernen.
@inproceedings{schmid_ea:1998b,
  author = {Ute Schmid and R. Mercy and Fritz Wysotzki},
  title = {Programming by analogy: Retrieval, Mapping, adaptation and
		  generalization of recursive program schemes},
  booktitle = {{FGML'98}: Proceedings of the {Annual Meeting of the GI
		  Machine Learning Group} (Technische Universit\"at, Berlin,
		  Aug.\,17.--19, 1998)},
  year = 1998,
  series = {Forschungsberichte des Fachbereichs Informatik},
  volume = 98,
  pages = {140--147},
  address = {TU Berlin},
  note = {Beitr{\"a}ge zum Treffen der GI-Fachgruppe 1.1.3
		  Maschinelles Lernen},
  number = 11
}
 
Ute Schmid, U. Sinha, and Fritz Wysotzki. Generalizing recursive program schemes with anti-unification. In GMD, editor, FGML'00: Proceedings of the Annual Meeting of the GI Machine Learning Group (St.Augustin, 18.-20.09.2000), pages 139-140, 2000. Beiträge zum Treffen der GI-Fachgruppe 1.1.3 Maschinelles Lernen.
@inproceedings{schmid_ea:2000,
  author = {Ute Schmid and U. Sinha and Fritz Wysotzki},
  title = {Generalizing Recursive Program Schemes with
		  Anti-Unification},
  editor = {GMD},
  booktitle = {{FGML'00}: Proceedings of the {Annual Meeting of the GI
		  Machine Learning Group} (St.\,Augustin, 18.--20.\,09.\,2000)},
  year = 2000,
  pages = {139--140},
  note = {Beitr{\"a}ge zum Treffen der GI-Fachgruppe 1.1.3
		  Maschinelles Lernen}
}
 
Ute Schmid, Emanuel Kitzelmann, and Fritz Wysotzki. Inductive program synthesis: From theory to application. In Gabriella Kókai and Jens Zeidler, editors, FGML'02: Proceedings of the Annual Meeting of the GI Machine Learning Group (Hannover, Germany, Oct.7-9, 2002), pages 135-141, 2002. Beiträge zum Treffen der GI-Fachgruppe 1.1.3 Maschinelles Lernen.
@inproceedings{schmid_ea:2002,
  author = {Ute Schmid and Emanuel Kitzelmann and Fritz Wysotzki},
  title = {Inductive Program Synthesis: From Theory to Application},
  editor = {Gabriella K\'okai and Jens Zeidler},
  booktitle = {{FGML'02}: Proceedings of the {Annual Meeting of the GI
		  Machine Learning Group (Hannover, Germany, Oct.\,7--9,
		  2002)}},
  year = 2002,
  pages = {135--141},
  note = {Beitr{\"a}ge zum Treffen der GI-Fachgruppe 1.1.3
		  Maschinelles Lernen},
  keywords = {2002; automatic programming; induction; inductive;
		  inductive functional programming; inductive inference;
		  inductive learning; inductive program synthesis; inductive
		  programming; inproceedings; machine learning; programming;
		  recursive program schemes},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/publications/fgml02.pdf}
}
 
Ute Schmid, Martin Hofmann, and Emanuel Kitzelmann. Analytical inductive programming as a cognitive rule acquisition device. In B. Goertzel, P. Hitzler, and M. Hutter, editors, Artificial General Intelligence. AGI'09: Proceedings of the 2nd Conference on Artificial General Intelligence (Arlington, Virginia, March6-9 2009), Advances in Intelligent Systems Research, pages 162-167. Atlantis Press, 2009.
@inproceedings{schmid_ea:2009,
  author = {Ute Schmid and Martin Hofmann and Emanuel Kitzelmann},
  title = {Analytical Inductive Programming as a Cognitive Rule
		  Acquisition Device},
  editor = {B. Goertzel and P. Hitzler and M. Hutter},
  booktitle = {Artificial General Intelligence. {AGI'09}: Proceedings of
		  the 2nd Conference on Artificial General Intelligence
		  (Arlington, Virginia, March\,6--9 2009)},
  year = 2009,
  series = {Advances in Intelligent Systems Research},
  pages = {162--167},
  publisher = {Atlantis Press},
  isbn = {978-90-78677-24-6},
  url = {http://dx.doi.org/10.2991/agi.2009.35},
  keywords = {inductive programming},
  abstract = {One of the most admirable characteristic of the human
		  cognitive system is its ability to extract generalized
		  rules covering regularities from example experience
		  presented by or experienced from the environment. Humans'
		  problem solving, reasoning and verbal behavior often shows
		  a high degree of systematicity and productivity which can
		  best be characterized by a competence level reflected by a
		  set of recursive rules. While we assume that such rules are
		  different for different domains, we believe that there
		  exists a general mechanism to extract such rules from only
		  positive examples from the environment. Our system Igor2 is
		  an analytical approach to inductive programming which
		  induces recursive rules by generalizing over regularities
		  in a small set of positive input/output examples. We
		  applied Igor2 to typical examples from cognitive do- mains
		  and can show that the Igor2 mechanism is able to learn the
		  rules which can best describe systematic and productive
		  behavior in such domains.}
}
 
Ute Schmid, Emanuel Kitzelmann, and Rinus Plasmeijer, editors. AAIP'09: Proceedings of the 3rd Workshop on Approaches and Applications of Inductive Programming (ICFP'09, Edinburgh, Scottland, Sept.4, 2009), number 81 in Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten Informatik. University of Bamberg, 2009. In conjunction with the 14th ACM SIGPLAN International Conference on Functional Programming (ICFP'09).
@proceedings{schmid_ea:2009b,
  title = {{AAIP'09}: Proceedings of the 3rd Workshop on Approaches
		  and Applications of Inductive Programming ({ICFP'09},
		  Edinburgh, Scottland, Sept.\,4, 2009)},
  year = 2009,
  editor = {Schmid, Ute and Kitzelmann, Emanuel and Plasmeijer,
		  Rinus},
  number = 81,
  series = {Bamberger Beitr\"age zur Wirtschaftsinformatik und
		  Angewandten Informatik},
  publisher = {University of Bamberg},
  note = {In conjunction with the 14th {ACM} {SIGPLAN} International
		  Conference on Functional Programming ({ICFP'09})},
  size = {120 pages}
}
 
Ute Schmid, Martin Hofmann, and Emanuel Kitzelmann. Inductive Programming. Example-driven Construction of Functional Programs. KI - Künstliche Intelligenz, 23(2):38-41, 2009.
@article{schmid_ea:2009c,
  author = {Ute Schmid and Martin Hofmann and Emanuel Kitzelmann},
  title = {{Inductive Programming. Example-driven Construction of
		  Functional Programs}},
  journal = {KI -- K{\"u}nstliche Intelligenz},
  year = 2009,
  volume = 23,
  number = 2,
  pages = {38--41},
  documenturl = {http://www.kuenstliche-intelligenz.de/fileadmin/template/main/archiv/pdf/ki2009-02_page38_web_teaser.pdf}
}
 
Ute Schmid, Martin Hofmann, Florian Bader, Tilmann Häberle, and Thomas Schneider. Incident Mining using Structural Prototypes. In Nicolás García-Pedrajas, Herrera Francisco, Colin Fyfe, José Manuel Benítez, and Moonis Ali, editors, Trends in Applied Intelligent Systems. 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE'10, Cordoba, Spain, June1-4, 2010. Proceedings, volume 6097 of Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, pages 327-336, Berlin/Heidelberg, 2010. Springer.
@inproceedings{schmid_ea:2010,
  author = {Ute Schmid and Martin Hofmann and Florian Bader and
		  Tilmann H{\"a}berle and Thomas Schneider},
  title = {{Incident Mining using Structural Prototypes}},
  editor = {Nicol{\'a}s Garc{\'i}a-Pedrajas and Herrera Francisco and
		  Colin Fyfe and Jos{\'e} Manuel Ben{\'i}tez and Moonis Ali},
  booktitle = {Trends in Applied Intelligent Systems. 23rd International
		  Conference on Industrial Engineering and Other Applications
		  of Applied Intelligent Systems, {IEA/AIE'10}, Cordoba,
		  Spain, June\,1--4, 2010. Proceedings},
  year = 2010,
  series = {Lecture Notes in Computer Science. Lecture Notes in
		  Artificial Intelligence},
  volume = 6097,
  pages = {327--336},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  isbn = {978-3-642-13021-2}
}
 
Ute Schmid, Emanuel Kitzelmann, and Rinus Plasmeijer, editors. Approaches and Applications of Inductive Programming. 3rd International Workshop, AAIP'09, Edinburgh, UK, Sept.4, 2009. Revised Papers, volume 5812 of Lecture Notes in Computer Science, Berlin/Heidelberg, 2010. Springer.
@proceedings{schmid_ea:2010b,
  title = {Approaches and Applications of Inductive Programming. 3rd
		  International Workshop, {AAIP'09}, Edinburgh, UK, Sept.\,4,
		  2009. Revised Papers},
  year = 2010,
  editor = {Ute Schmid and Emanuel Kitzelmann and Rinus Plasmeijer},
  volume = 5812,
  series = {Lecture Notes in Computer Science},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-11930-9},
  url = {http://www.springerlink.com/content/r4r654707444/},
  doi = {10.1007/978-3-642-11931-6},
  keywords = {functional programming; inductive programming}
}
 
Jürgen Schmidhuber. Optimal ordered problem solver. Machine Learning, 54(3):211-254, March 2004.
@article{schmidhuber:2004,
  author = {J\"urgen Schmidhuber},
  title = {Optimal Ordered Problem Solver},
  journal = {Machine Learning},
  year = 2004,
  volume = 54,
  number = 3,
  pages = {211--254},
  month = {March},
  publisher = {Springer},
  address = {Netherlands},
  issn = {0885-6125 (Print) 1573-0565 (Online)},
  url = {http://www.springerlink.com/content/l6v96242k51117w3/},
  doi = {10.1023/B:MACH.0000015880.99707.b2},
  keywords = {enumerative ip; induction; inductive programming;
		  metalearning; oops; program synthesis},
  abstract = {We introduce a general and in a certain sense time-optimal
		  way of solving one problem after another, efficiently
		  searching the space of programs that compute solution
		  candidates, including those programs that organize and
		  manage and adapt and reuse earlier acquired knowledge. The
		  Optimal Ordered Problem Solver (OOPS) draws inspiration
		  from Levin's Universal Search designed for single problems
		  and universal Turing machines. It spends part of the total
		  search time for a new problem on testing programs that
		  exploit previous solution-computing programs in computable
		  ways. If the new problem can be solved faster by
		  copy-editing/invoking previous code than by solving the new
		  problem from scratch, then OOPS will find this out. If not,
		  then at least the previous solutions will not cause much
		  harm. We introduce an efficient, recursive,
		  backtracking-based way of implementing OOPS on realistic
		  computers with limited storage. Experiments illustrate how
		  OOPS can greatly profit from metalearning or metasearching,
		  that is, searching for faster search procedures.}
}
 
Jürgen Schmidhuber. How to learn a program: Optimal universal learners & goedel machines. In Emanuel Kitzelmann, Roland J. Olsson, and Ute Schmid, editors, AAIP'05: Proceedings of the 1st Workshop on Approaches and Applications of Inductive Programming (Bonn, Germany, Aug.7, 2005), page 11, 2005. Invited Talk Abstract.
@inproceedings{schmidhuber:2005,
  author = {J\"urgen Schmidhuber},
  title = {How to Learn a Program: Optimal Universal Learners &
		  Goedel Machines },
  editor = {Emanuel Kitzelmann and Roland J. Olsson and Ute Schmid},
  booktitle = {{AAIP'05}: Proceedings of the 1st Workshop on Approaches
		  and Applications of Inductive Programming (Bonn, Germany,
		  Aug.\,7, 2005)},
  year = 2005,
  pages = 11,
  note = {Invited Talk Abstract},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip05/proceedings/abs_schmidhuber.pdf}
}
 
Uwe Schöning. Logic for Computer Scientists. Modern Birkhäuser Classics. Birkhäuser Boston, 2008.
@book{schoening:2008,
  author = {Uwe Sch\"{o}ning},
  title = {Logic for Computer Scientists},
  publisher = {Birkh\"{a}user Boston},
  year = 2008,
  series = {Modern Birkh\"{a}user Classics},
  keywords = {logic},
  isbn = 0817647627
}
 
Stefan Schrödl and Stefan Edelkamp. Inferring flow of control in program synthesis by example. In KI-99: Advances in Artificial Intelligence. 23rd Annual German Conference on Artificial Intelligence, Bonn, Germany, Sept.13-15, 1999. Proceedings, volume 1701 of Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, pages 171-182, Berlin/Heidelberg, 1999. Springer.
@inproceedings{schroedl/edelkamp:1999,
  author = {Stefan Schr{\"o}dl and Stefan Edelkamp},
  title = {Inferring Flow of Control in Program Synthesis by
		  Example},
  booktitle = {{KI-99}: Advances in Artificial Intelligence. 23rd Annual
		  German Conference on Artificial Intelligence, Bonn,
		  Germany, Sept.\,13--15, 1999. Proceedings},
  year = 1999,
  series = {Lecture Notes in Computer Science. Lecture Notes in
		  Artificial Intelligence},
  volume = 1701,
  pages = {171--182},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-66495-6},
  url = {http://www.springerlink.com/content/dqcw6kjnmw3gdwh5/},
  abstract = {Abstract. We present a supervised, interactive learning
		  technique that infers control structures of computer
		  programs from user-demonstrated traces. A two-stage process
		  is applied: first, a minimal deterministic finite automaton
		  (DFA)Mlabeled by the instructions of the program is learned
		  from a set of example traces and membership queries to the
		  user. It accepts all preffixes of traces of the target
		  program. The number of queries is bounded byO(k|M|),
		  withkbeing the total number of instructions in the initial
		  example traces. In the second step we parse this automaton
		  into a high-level programming language inO(|M|2) steps,
		  replacing jumps by conditional control structures.},
  doi = {10.1007/3-540-48238-5_14}
}
 
Ehud Y. Shapiro. An algorithm that infers theories from facts. In A. Drinan, editor, IJCAI'81: Proceedings of the 7th International Joint Conference on Artificial Intelligence (Vancouver, BC, Canada, Aug.24-28, 1981), pages 446-451, Los Altos, CA, 1981. Morgan Kaufmann.
@inproceedings{shapiro:1981,
  author = {Ehud Y. Shapiro},
  title = {An Algorithm that Infers Theories from Facts},
  editor = {A. Drinan},
  booktitle = {{IJCAI}'81: Proceedings of the 7th International Joint
		  Conference on Artificial Intelligence (Vancouver, BC,
		  Canada, Aug.\,24--28, 1981)},
  year = 1981,
  pages = {446--451},
  address = {Los Altos, CA},
  publisher = {Morgan Kaufmann},
  keywords = {ilp; inductive programming; machine learning; mis; seminal
		  paper},
  annote = {short version of the tech-report: Inductive Inference of
		  Theories from Facts, 1981, Yale Univ.}
}
 
Ehud Y. Shapiro. Algorithmic Program Debugging. MIT Press, 1983.
@book{shapiro:1983,
  author = {Ehud Y. Shapiro},
  title = {Algorithmic Program Debugging},
  publisher = {MIT Press},
  year = 1983,
  keywords = {book; debugging; ilp; mis},
  annote = {Shapiro's PhD dissertation}
}
 
Jude W. Shavlik. Acquiring recursive and iterative concepts with explanation-based learning. Machine Learning, 5(1):39-70, March 1990.
@article{shavlik:1990,
  author = {Jude W. Shavlik},
  title = {Acquiring recursive and iterative concepts with
		  explanation-based learning},
  journal = {Machine Learning},
  year = 1990,
  volume = 5,
  number = 1,
  pages = {39--70},
  month = {March},
  publisher = {Springer},
  address = {Netherlands},
  issn = {0885-6125 (Print) 1573-0565 (Online)},
  url = {http://www.springerlink.com/content/l09626h1ng137737/},
  abstract = {Inexplanation-based learning, a specific problem''s
		  solution is generalized into a form that can be later used
		  to solve conceptually similar problems. Most research in
		  explanation-based learning involves relaxing constraints on
		  the variables in the explanation of a specific example,
		  rather than generalizing thegraphical structureof the
		  explanation itself. However, this precludes the acquisition
		  of concepts where an iterative or recursive process is
		  implicitly represented in the explanation by a fixed number
		  of applications. This paper presents an algorithm that
		  generalizes explanation structures and reports empirical
		  results that demonstrate the value of acquiring recursive
		  and iterative concepts. The BAGGER2 algorithm learns
		  recursive and iterative concepts, integrates results from
		  multiple examples, and extracts useful subconcepts during
		  generalization. On problems where learning a recursive rule
		  is not appropriate, the system produces the same result as
		  standard explanation-based methods. Applying the learned
		  recursive rules only requires a minor extension to a
		  PROLOG-like problem solver, namely, the ability to
		  explicitly call a specific rule. Empirical studies
		  demonstrate that generalizing the structure of explanations
		  helps avoid the recently reported negative effects of
		  learning.},
  doi = {10.1023/A:1022659708512},
  keywords = {Explanation-based generalization; generalizing explanation
		  structures; generalizing to N; generalizing number; utility
		  of learning; operationality versus generality}
}
 
D. Shaw, W. Swartout, and C. Green. Inferring LISP programs from examples. In IJCAI'75: Advance Papers of the 4th International Joint Conference on Artificial Intelligence (Tbilisi, Georgia, USSR,Sept.3-8, 1975), pages 260-267, 1975.
@inproceedings{shaw_ea:1975,
  author = {D. Shaw and W. Swartout and C. Green},
  title = {Inferring {LISP} Programs from Examples},
  booktitle = {{IJCAI}'75: Advance Papers of the 4th International Joint
		  Conference on Artificial Intelligence (Tbilisi, Georgia,
		  USSR,Sept.\,3--8, 1975)},
  year = 1975,
  pages = {260--267},
  documenturl = {http://dli.iiit.ac.in/ijcai/IJCAI-75-VOL-1&2/PDF/037.pdf},
  keywords = {ifp; induction; inductive programming; lisp; pre-summers;
		  program synthesis}
}
 
Tim Sheard and Leonidas Fegaras. A fold for all seasons. In FPCA'93: Proceedings of the 6th ACM SIGPLAN/SIGARCH International Conference on Functional Programming Languages and Computer Architecture (Copenhagen, Denmark, June9-11, 1993). ACM Press, 1993.
@inproceedings{sheard/fegaras:1993,
  author = {Tim Sheard and Leonidas Fegaras},
  title = {A Fold for All Seasons},
  booktitle = {{FPCA'93}: Proceedings of the 6th {ACM} {SIGPLAN/SIGARCH}
		  International Conference on Functional Programming
		  Languages and Computer Architecture (Copenhagen, Denmark,
		  June\,9--11, 1993)},
  year = 1993,
  publisher = {ACM Press},
  isbn = {0-89791-595-X},
  annote = {ute-inflit}
}
 
L. Siklossy and D. A. Sykes. Automatic program synthesis from example problems. In IJCAI'75: Advance Papers of the 4th International Joint Conference on Artificial Intelligence (Tbilisi, Georgia, USSR,Sept.3-8, 1975), pages 268-273, 1975.
@inproceedings{siklossy/sykes:1975,
  author = {L. Siklossy and D. A. Sykes},
  title = {Automatic Program Synthesis from Example Problems},
  booktitle = {{IJCAI}'75: Advance Papers of the 4th International Joint
		  Conference on Artificial Intelligence (Tbilisi, Georgia,
		  USSR,Sept.\,3--8, 1975)},
  year = 1975,
  pages = {268--273},
  documenturl = {http://dli.iiit.ac.in/ijcai/IJCAI-75-VOL-1&2/PDF/038.pdf},
  keywords = {ifp; induction; inductive programming; lisp; pre-summers;
		  program synthesis}
}
 
A. Smaill and I. Green. Automating the synthesis of functional programs, 1995.
@misc{smaill/green:1995,
  author = {A. Smaill and I. Green},
  title = {Automating the synthesis of functional programs},
  year = 1995,
  institution = {Department of Artificial Intelligence, University of
		  Edinburgh},
  number = {Research paper 777},
  annote = {ute-inflit}
}
 
Douglas R. Smith. The synthesis of LISP programs from examples: A survey. In Alan W. Biermann, Yves Kodratoff, and Gérard Guiho, editors, Automatic Program Construction Techniques, pages 307-324. The Free Press, New York, NY, USA, 1984.
@incollection{smith:1984,
  author = {Smith, Douglas R.},
  title = {The Synthesis of {LISP} Programs from Examples: A Survey},
  editor = {Alan W. Biermann and Yves Kodratoff and G\'erard Guiho},
  booktitle = {Automatic Program Construction Techniques},
  publisher = {The Free Press},
  year = 1984,
  pages = {307--324},
  address = {New York, NY, USA},
  isbn = 0029490707,
  keywords = {analytical ip; comparison; enumerative ip; ifp; induction;
		  inductive programming; program synthesis; survey},
  annote = {a good survey of classical inductive synthesis from
		  traces, summers, biermann etc.}
}
 
E. Soloway and J. C. Spohrer, editors. Studying the Novice Programmer. Lawrence Erlbaum, Hillsdale, NJ, 1989.
@book{soloway/spohrer:1989,
  editor = {E. Soloway and J. C. Spohrer},
  title = {Studying the Novice Programmer},
  publisher = {Lawrence Erlbaum},
  year = 1989,
  address = {Hillsdale, NJ}
}
 
Irene Stahl and Irene Weber. The arguments of newly invented predicates in ILP. In ILP'94: Proceedings of the 4th International Workshop on Inductive Logic Programming (Bonn, Germany, Sept.12-14, 1994), volume 237 of GMD-Studien. Gesellschaft für Mathematik und Datenverarbeitung MBH, 1994.
@inproceedings{stahl/weber:1994,
  author = {Irene Stahl and Irene Weber},
  title = {The Arguments of Newly Invented Predicates in {ILP}},
  booktitle = {{ILP'94}: Proceedings of the 4th International Workshop on
		  Inductive Logic Programming (Bonn, Germany, Sept.\,12--14,
		  1994)},
  year = 1994,
  series = {{GMD}-Studien},
  volume = 237,
  publisher = {{G}esellschaft f{\"{u}}r {M}athematik und
		  {D}atenverarbeitung {MBH}},
  keywords = {ilp; induction; inductive programming; predicate
		  invention; program synthesis}
}
 
Irene Stahl. Predicate invention in ilp - an overview. In Pavel Brazdil, editor, Machine Learning: ECML-93. European Conference on Machine Learning Vienna, Austria, April5-7, 1993. Proceedings, volume 667 of Lecture Notes in Computer Science, pages 311-322. Springer, 1993.
@inproceedings{stahl:1993,
  author = {Irene Stahl},
  title = {Predicate invention in ILP -- an overview},
  editor = {Pavel Brazdil},
  booktitle = {Machine Learning: {ECML-93}. European Conference on
		  Machine Learning Vienna, Austria, April\,5--7, 1993.
		  Proceedings},
  year = 1993,
  series = {Lecture Notes in Computer Science},
  volume = 667,
  pages = {311--322},
  publisher = {Springer},
  keywords = {ilp; induction; inductive programming; overview; predicate
		  invention; program synthesis},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-56602-1},
  url = {http://www.springerlink.com/content/70510v0l339q226j/},
  abstract = {Inductive Logic Programming (ILP) is a subfield of machine
		  learning dealing with inductive inference in a first order
		  Horn clause framework. A problem in ILP is how to extend
		  the hypotheses language in the case that the vocabulary
		  given initially is insufficient. One way to adapt the
		  vocabulary is to introducenew predicates.},
  doi = {10.1007/3-540-56602-3_144}
}
 
Irene Stahl. On the utility of predicate invention in inductive logic programming. In Francesco Bergadano and Luc De Raedt, editors, Machine Learning: ECML-94. European Conference on Machine Learning, Catania, Italy, April6-8, 1994. Proceedings, volume 784 of Lecture Notes in Computer Science, pages 272-286, Berlin/Heidelberg, 1994. Springer.
@inproceedings{stahl:1994,
  author = {Irene Stahl},
  title = {On the utility of predicate invention in inductive logic
		  programming},
  editor = {Bergadano, Francesco and De~Raedt, Luc},
  booktitle = {Machine Learning: {ECML-94}. European Conference on
		  Machine Learning, Catania, Italy, April\,6--8, 1994.
		  Proceedings},
  year = 1994,
  series = {Lecture Notes in Computer Science},
  volume = 784,
  pages = {272--286},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-57868-0},
  url = {http://www.springerlink.com/content/f021737t2325313p/},
  abstract = {The task of predicate invention in ILP is to extend the
		  hypothesis language with new predicates in case that the
		  vocabulary given initially is insufficient for the learning
		  task. However, whether predicate invention really helps to
		  make learning succeed in the extended language depends on
		  the bias that is currently employed.},
  doi = {10.1007/3-540-57868-4_64}
}
 
Irene Stahl. The appropriateness of predicate invention as bias shift operation in ILP. Machine Learning, 20(1-2):95-117, July 1995.
@article{stahl:1995,
  author = {Irene Stahl},
  title = {The Appropriateness of Predicate Invention as Bias Shift
		  Operation in {ILP}},
  journal = {Machine Learning},
  year = 1995,
  volume = 20,
  number = {1-2},
  pages = {95--117},
  month = {July},
  keywords = {Inductive Logic Programming; Bias Shift; Predicate
		  Invention},
  publisher = {Springer},
  address = {Netherlands},
  issn = {0885-6125 (Print) 1573-0565 (Online)},
  url = {http://www.springerlink.com/content/k322175786h1j628/},
  abstract = {The task of predicate invention in Inductive Logic
		  Programming is to extend the hypothesis language with new
		  predicates if the vocabulary given initially is
		  insufficient for the learning task. However, whether
		  predicate invention really helps to make learning succeed
		  in the extended language depends on the language bias
		  currently employed.In this paper, we investigate for which
		  commonly employed language biases predicate invention is an
		  appropriate shift operation. We prove that for some
		  restricted languages predicate invention does not help when
		  the learning task fails and we characterize the languages
		  for which predicate invention is useful. We investigate the
		  decidability of the bias shift problem for these languages
		  and discuss the capabilities of predicate invention as a
		  bias shift operation.},
  doi = {10.1023/A:1022638219164}
}
 
Irene Stahl. Predicate invention in inductive logic programming. In Luc De Raedt, editor, Advances in Inductive Logic Programming, pages 34-47. IOS Press, 1996.
@incollection{stahl:1996,
  author = {Irene Stahl},
  title = {Predicate Invention in Inductive Logic Programming},
  editor = {De~Raedt, Luc},
  booktitle = {Advances in Inductive Logic Programming},
  publisher = {IOS Press},
  year = 1996,
  pages = {34--47},
  keywords = {ilp; induction; inductive programming; overview; predicate
		  invention; program synthesis}
}
 
Wolfgang Stolzmann. An introduction to anticipatory classifier systems. In Learning Classifier Systems. From Foundations to Applications, volume 1813 of Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, pages 175-194. Springer, Berlin/Heidelberg, 2000.
@incollection{stolzmann:2000,
  author = {Wolfgang Stolzmann},
  title = {An Introduction to Anticipatory Classifier Systems},
  booktitle = { Learning Classifier Systems. From Foundations to
		  Applications},
  publisher = {Springer},
  year = 2000,
  volume = 1813,
  series = {Lecture Notes in Computer Science. Lecture Notes in
		  Artificial Intelligence},
  pages = {175--194},
  address = {Berlin\,/\,Heidelberg},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-67729-1},
  url = {http://www.springerlink.com/content/gclp9mtkkggk0vhv/},
  doi = {10.1007/3-540-45027-0_9},
  keywords = {acs},
  abstract = {Anticipatory Classifier Systems (ACS) are classifier
		  systems that learn by using the cognitive mechanism of
		  anticipatory behavioral control which was introduced in
		  cognitive psychology by Hoffmann [4]. They can learn in
		  deterministic multi-step environments.1 A stepwise
		  introduction to ACS is given. We start with the basic
		  algorithm and apply it in simple ``woods'' environments.
		  Itwill be shown that this algorithm can only learn in a
		  special kind of deterministic multi-step environments. Two
		  extensionsare discussed. The first one enables an ACS to
		  learn in any deterministic multi-step environment. The
		  second one allows anACS to deal with a special kind of
		  non-Markov state.}
}
 
Phillip D. Summers. Program Construction from Examples. PhD thesis, Department of Computer Science, Yale University, New Haven, US-CT, 1975.
@phdthesis{summers:1975,
  author = {Phillip D. Summers},
  title = {Program Construction from Examples},
  school = {Department of Computer Science, Yale University},
  year = 1975,
  address = {New Haven, US-CT},
  keywords = {analytical ip; ifp; induction; inductive programming;
		  ip-system; lisp; program synthesis; thesys}
}
 
Phillip D. Summers. A methodology for LISP program construction from examples. Journal of the ACM, 24(1):161-175, January 1977.
@article{summers:1977,
  author = {Phillip D. Summers},
  title = {A Methodology for {LISP} Program Construction from
		  Examples},
  journal = {Journal of the {ACM}},
  year = 1977,
  volume = 24,
  number = 1,
  pages = {161--175},
  month = {January},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  url = {http://doi.acm.org/10.1145/321992.322002},
  keywords = {analytical ip; article; ifp; induction; inductive
		  programming; program synthesis; seminal paper; thesys},
  annote = {The inductive programming seminal paper from Summers.
		  Constructing a linear recursive program by generalising
		  regularities in a finite set of traces and predicates.},
  abstract = {An automatic programming system, THESYS, for constructing
		  recursive LISP programs from examples of what they do is
		  described. The construction methodology is illustrated as a
		  series of transformations from the set of examples to a
		  program satisfying the examples. The transformations
		  consist of (1) deriving the specific computation associated
		  with a specific example, (2) deriving control flow
		  predicates, and (3) deriving an equivalent program
		  specification in the form of recurrence relations.
		  Equivalence between certain recurrence relations and
		  various program schemata is proved. A detailed description
		  of the construction of four programs is presented to
		  illustrate the application of the methodology.}
}
 
Lappoon R. Tang, Mary E Califf, and Raymond J. Mooney. An experimental comparison of genetic programming and inductive logic programming on learning recursive list functions. Technical Report AI-98-271, University of Texas at Austin, Austin, TX, USA, 1998.
@techreport{tang_ea:1998,
  author = {Lappoon R. Tang and Mary E Califf and Raymond J. Mooney},
  title = {An Experimental Comparison of Genetic Programming and
		  Inductive Logic Programming on Learning Recursive List
		  Functions},
  institution = {University of Texas at Austin},
  year = 1998,
  number = {AI-98-271},
  address = {Austin, TX, USA},
  documenturl = {http://www.cs.utexas.edu/~ml/papers/ilpgp-ml-98.pdf},
  keywords = {comparison; enumerative ip; experiment; gp; ifp; ilp;
		  induction; inductive programming; program evolution;
		  program synthesis},
  abstract = {This paper experimentally compares three approaches to
		  program induction: inductive logic programming (ILP),
		  genetic programming (GP), and genetic logic programming
		  (GLP) (a variant of GP for inducing Prolog programs). Each
		  of these methods was used to induce four simple, recursive,
		  list-manipulation functions. The results indicate that ILP
		  is the most likely to induce a correct program from small
		  sets of random examples, while GP is generally less
		  accurate. GLP performs the worst, and is rarely able to
		  induce a correct program. Interpretations of these results
		  in terms of differences in search methods and inductive
		  biases are presented.}
}
 
Terese. Term Rewriting Systems, volume 55 of Cambridge Tracts in Theoretical Computer Science. Cambridge University Press, 2003.
@book{terese:2003,
  author = {Terese},
  title = {Term Rewriting Systems},
  publisher = {Cambridge University Press},
  year = 2003,
  volume = 55,
  series = {Cambridge Tracts in Theoretical Computer Science},
  keywords = {book; term rewriting}
}
 
J. Toussaint, Ute Schmid, and Fritz Wysotzki. Using recursive control rules in planning. In H. R. Arabnia, editor, ICAI'10: Proceedings of the 12th International Conference on Artificial Intelligence (Las Vegas, Nevada, USA, July12-15, 2010), volume 2, pages 1012-1015, Las Vegas, 2001. CSREA Press.
@inproceedings{toussaint_ea:2001,
  author = {J. Toussaint and Ute Schmid and Fritz Wysotzki},
  title = {Using Recursive Control Rules in Planning},
  editor = {H. R. Arabnia},
  booktitle = {{ICAI'10}: Proceedings of the 12th International
		  Conference on Artificial Intelligence (Las Vegas, Nevada,
		  USA, July\,12--15, 2010)},
  year = 2001,
  volume = 2,
  pages = {1012--1015},
  address = {Las Vegas},
  publisher = {{CSREA} Press}
}
 
Paul E. Utgoff. Shift of bias for inductive concept learning. In Ryszard S. Michalski, Jaime G. Carbonell, and Tom M. Mitchell, editors, Machine Learning. An Artificial Intelligence Approach, volume 2, chapter 5, pages 107-148. Morgan Kaufmann, Los Altos, CA, 1986.
@incollection{utgoff:1986,
  author = {Paul E. Utgoff},
  title = {Shift of Bias for Inductive Concept Learning},
  editor = {Ryszard S. Michalski and Jaime G. Carbonell and Tom M.
		  Mitchell},
  booktitle = {Machine Learning. An Artificial Intelligence Approach},
  publisher = {Morgan Kaufmann},
  year = 1986,
  volume = 2,
  chapter = 5,
  pages = {107--148},
  address = {Los Altos, CA},
  keywords = {bias; bias-shift; machine learning}
}
 
Tarmo Uustalu, Varmo Vene, and Alberto Pardo. Recursion schemes from comonads. Nordic Journal of Computing, 8(3):366-390, 2001.
@article{uustalu_ea:2001,
  author = {Uustalu, Tarmo and Vene, Varmo and Pardo, Alberto},
  title = {Recursion schemes from comonads},
  journal = {Nordic Journal of Computing},
  year = 2001,
  volume = 8,
  number = 3,
  pages = {366--390},
  address = {Finland},
  issn = {1236-6064},
  publisher = {Publishing Association Nordic Journal of Computing}
}
 
L. G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, 1984.
@article{valiant:1984,
  author = {L. G. Valiant},
  title = {A Theory of the Learnable},
  journal = {Communications of the {ACM}},
  year = 1984,
  volume = 27,
  number = 11,
  pages = {1134--1142},
  address = {New York, NY, USA},
  publisher = {{ACM}},
  url = {http://doi.acm.org/10.1145/1968.1972},
  keywords = {induction; machine learning; pac-learning; seminal paper},
  annote = {Valiants seminal paper on computational learning theory.}
}
 
Antonio Varlaro, Margherita Berardi, and Donato Malerba. Learning recursive theories with the separate-and-parallel conquer strategy. In Proceedings of the Workshop on Advances in Inductive Rule Learning (Pisa, Italy, Sept.20-24, 2004), pages 179-193, 2004. In conjunction with ECML/PKDD.
@inproceedings{varlaro_ea:2004,
  author = {Varlaro, Antonio and Berardi, Margherita and Malerba,
		  Donato},
  title = {Learning recursive theories with the separate-and-parallel
		  conquer strategy},
  booktitle = {Proceedings of the Workshop on Advances in Inductive Rule
		  Learning (Pisa, Italy, Sept.\,20--24, 2004)},
  year = 2004,
  pages = {179--193},
  note = {In conjunction with ECML/PKDD}
}
 
Geir Vattekar. ADATE User Manual, March 2006.
@manual{vattekar:2006,
  author = {Geir Vattekar},
  title = {ADATE User Manual},
  month = {March},
  year = 2006,
  school = {\O stfold University College}
}
 
B. Wegbreit. Goal-directed program transformation. IEEE Transactions on Software Engineering, 2(2):69-80, 1976.
@article{wegbreit:1976,
  author = {B. Wegbreit},
  title = {Goal-Directed Program Transformation},
  journal = {IEEE Transactions on Software Engineering},
  year = 1976,
  volume = 2,
  number = 2,
  pages = {69--80},
  address = {Los Alamitos, CA, USA},
  publisher = {IEEE Computer Society},
  keywords = {program transformation},
  url = {http://doi.ieeecomputersociety.org/10.1109/TSE.1976.233533},
  abstract = {Program development often proceeds by transforming simple,
		  clear programs into complex, involuted, but more efficient
		  ones. This paper examines ways this process can be rendered
		  more systematic. We show how analysis of program
		  performance, partial evaluation of functions, and
		  abstraction of recursive function definitions from
		  recurring subgoals can be combined to yield many global
		  transformations in a methodical fashion. Examples are drawn
		  from compiler optimization, list processing, very high-evel
		  languages, and APL execution.}
}
 
Donald S. Williams. Computer program organization induced from problem examples. In Herbert A. Simon and Laurent Siklossy, editors, Representation and Meaning: Experiments with Information Processing Systems, chapter 4, pages 143-206. Prentice-Hall, Englewood Cliffs, NJ, 1972.
@incollection{williams:1972,
  author = {Donald S. Williams},
  title = {Computer program organization induced from problem
		  examples},
  editor = {Herbert A. Simon and Laurent Siklossy},
  booktitle = {Representation and Meaning: Experiments with Information
		  Processing Systems},
  publisher = {Prentice-Hall},
  year = 1972,
  chapter = 4,
  pages = {143--206},
  address = {Englewood Cliffs, NJ}
}
 
R. S. Williams. Learning to program by examining and modifying cases. In John E. Laird, editor, ICML'88: Proceedings of the 5th International Conference on Machine Learning (Ann Arbor, Michigan, USA, June12-14, 1988), pages 318-324. Morgan Kaufmann, 1988.
@inproceedings{williams:1988,
  author = {R. S. Williams},
  title = {Learning to program by examining and modifying cases},
  editor = {John E. Laird},
  booktitle = {{ICML'88}: Proceedings of the 5th International Conference
		  on Machine Learning (Ann Arbor, Michigan, USA,
		  June\,12--14, 1988)},
  year = 1988,
  pages = {318--324},
  publisher = {Morgan Kaufmann},
  isbn = {0-934613-64-8},
  annote = {ute-inflit}
}
 
R. S. Williams. Learning to program by examining and modifying cases. In J. L. Kolodner, editor, Proceedings of the DARPA Workshop on Case-Based Reasoning (San Mateo, CAL, May 1988), pages 463-474. Morgan Kaufmann, 1988.
@inproceedings{williams:1988b,
  author = {R. S. Williams},
  title = {Learning to program by examining and modifying cases},
  editor = {J. L. Kolodner},
  booktitle = {Proceedings of the DARPA Workshop on Case-Based Reasoning
		  (San Mateo, CAL, May 1988)},
  year = 1988,
  pages = {463--474},
  publisher = {Morgan Kaufmann}
}
 
Man Wong and Tuen Mun. Evolving recursive programs by using adaptive grammar based genetic programming. Genetic Programming and Evolvable Machines, 6(4):421-455, December 2005.
@article{wong/mun:2005,
  author = {Man Wong and Tuen Mun},
  title = {Evolving Recursive Programs by Using Adaptive Grammar
		  Based Genetic Programming},
  journal = {Genetic Programming and Evolvable Machines},
  year = 2005,
  volume = 6,
  number = 4,
  pages = {421--455},
  month = {December},
  publisher = {Springer},
  address = { etherlands},
  issn = {1389-2576 (Print) 1573-7632 (Online)},
  url = {http://www.springerlink.com/content/y66h33rp510w43l4/},
  doi = {10.1007/s10710-005-4805-8},
  keywords = {recursive programs; logic grammars; grammar based genetic
		  programming; enumerative ip; gbgp; gp; induction; inductive
		  programming; program evolution; program synthesis},
  abstract = {Genetic programming (GP) extends traditional genetic
		  algorithms to automatically induce computer programs. GP
		  has been applied in a wide range of applications such as
		  software re-engineering, electrical circuits synthesis,
		  knowledge engineering, anddata mining. One of the most
		  important and challenging research areas in GP is the
		  investigation of ways to successfully evolverecursive
		  programs. A recursive program is one that calls itself
		  either directly or indirectly through other programs.
		  Becauserecursions lead to compact and general programs and
		  provide a mechanism for reusing program code, they
		  facilitate GP to solvelarger and more complicated problems.
		  Nevertheless, it is commonly agreed that the recursive
		  program learning problem is verydifficult for GP. In this
		  paper, we propose techniques to tackle the difficulties in
		  learning recursive programs. The techniquesare incorporated
		  into an adaptive Grammar Based Genetic Programming system
		  (adaptive GBGP). A number of experiments have beenperformed
		  to demonstrate that the system improves the effectiveness
		  and efficiency in evolving recursive programs.}
}
 
Stefan Wrobel. First order theory refinement. In Luc De Raedt, editor, Advances in Inductive Logic Programming, pages 14-33. IOS Press, 1996.
@incollection{wrobel:1996,
  author = {Stefan Wrobel},
  title = {First Order Theory Refinement},
  editor = {De~Raedt, Luc},
  booktitle = {Advances in Inductive Logic Programming},
  publisher = {IOS Press},
  year = 1996,
  pages = {14--33},
  keywords = {ilp; theory revision}
}
 
Fritz Wysotzki and Ute Schmid. Synthesis of recursive programs from finite examples by detection of macro-functions. Technical Report 1-2, TU Berlin, Berlin, 2001.
@techreport{wysotzki/schmid:2001,
  author = {Wysotzki, Fritz and Schmid, Ute},
  title = {Synthesis of Recursive Programs from Finite Examples by
		  Detection of Macro-Functions},
  institution = {TU Berlin},
  year = 2001,
  number = {1--2},
  address = {Berlin}
}
 
Fritz Wysotzki. Representation and induction of infinite concepts and recursive action sequences. In Alan Bundy, editor, IJCAI'83: Proceedings of the 8th International Joint Conference on Artificial Intelligence (Karlsruhe, Germany, Aug.,1983), pages 409-414. Morgan Kaufmann, 1983.
@inproceedings{wysotzki:1983,
  author = {Fritz Wysotzki},
  title = {Representation and induction of infinite concepts and
		  recursive action sequences},
  editor = {Alan Bundy},
  booktitle = {{IJCAI}'83: Proceedings of the 8th International Joint
		  Conference on Artificial Intelligence (Karlsruhe, Germany,
		  Aug.,1983)},
  year = 1983,
  pages = {409--414},
  publisher = {Morgan Kaufmann}
}
 
Fritz Wysotzki. Program synthesis by hierarchical planning. In P. Jorrand and V. Sgurev, editors, Artificial Intelligence: Methodology, Systems, Applications, pages 3-11. Elsevier, Amsterdam, 1987.
@incollection{wysotzki:1987,
  author = {Fritz Wysotzki},
  title = {Program synthesis by hierarchical planning},
  editor = {P. Jorrand and V. Sgurev},
  booktitle = {Artificial Intelligence: Methodology, Systems,
		  Applications},
  publisher = {Elsevier},
  year = 1987,
  pages = {3--11},
  address = {Amsterdam},
  annote = {ute-inflit}
}
 
Fritz Wysotzki. Development of inductive synthesis of functional programs. In Emanuel Kitzelmann, Roland J. Olsson, and Ute Schmid, editors, AAIP'05: Proceedings of the 1st Workshop on Approaches and Applications of Inductive Programming (Bonn, Germany, Aug.7, 2005), page 13, 2005. Invited Talk Abstract.
@inproceedings{wysotzki:2005,
  author = {Fritz Wysotzki},
  title = {Development of Inductive Synthesis of Functional
		  Programs},
  editor = {Emanuel Kitzelmann and Roland J. Olsson and Ute Schmid},
  booktitle = {{AAIP'05}: Proceedings of the 1st Workshop on Approaches
		  and Applications of Inductive Programming (Bonn, Germany,
		  Aug.\,7, 2005)},
  year = 2005,
  pages = 13,
  note = {Invited Talk Abstract},
  documenturl = {http://www.cogsys.wiai.uni-bamberg.de/aaip05/proceedings/abs_wysotzki.pdf}
}
 
Alexey Rodriguez Yakushev and Johan Jeuring. Enumerating well-typed terms generically. In Ute Schmid, Emanuel Kitzelmann, and Rinus Plasmeijer, editors, Approaches and Applications of Inductive Programming. 3rd International Workshop, AAIP'09, Edinburgh, UK, Sept.4, 2009. Revised Papers, volume 5812 of Lecture Notes in Computer Science, pages 93-116, Berlin/Heidelberg, 2010. Springer.
@inproceedings{yakushev/jeuring:2010,
  author = {Alexey Rodriguez Yakushev and Johan Jeuring},
  title = {Enumerating Well-Typed Terms Generically},
  editor = {Ute Schmid and Emanuel Kitzelmann and Rinus Plasmeijer},
  booktitle = {Approaches and Applications of Inductive Programming. 3rd
		  International Workshop, {AAIP'09}, Edinburgh, UK, Sept.\,4,
		  2009. Revised Papers},
  year = 2010,
  series = {Lecture Notes in Computer Science},
  volume = 5812,
  pages = {93--116},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-642-11930-9},
  url = {http://www.springerlink.com/content/m921756m170166p3/},
  abstract = {We use generic programming techniques to generate
		  well-typed lambda terms. We encode well-typed terms by
		  means of generalized algebraic datatypes (GADTs) and
		  existential types. The Spine approach to generic
		  programming supports GADTs, but it does not support the
		  definition of generic producers for existentials. We
		  describe how to extend the Spine approach to support
		  existentials and we use the improved Spine to define a
		  generic enumeration function. We show that the enumeration
		  function can be used to generate the terms of simply typed
		  lambda calculus.},
  doi = {10.1007/978-3-642-11931-6_5},
  documenturl = {http://www.springerlink.com/content/m921756m170166p3/fulltext.pdf}
}
 
Akihiro Yamamoto. Which hypotheses can be found with inverse entailment? In Nada Lavrač and Sašo Džeroski, editors, Inductive Logic Programming. 7th International Workshop, ILP'97, Prague, Czech Republic, Sept.17-20, 1997, Proceedings, volume 1297 of Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, pages 296-308, Berlin/Heidelberg, 1997. Springer.
@inproceedings{yamamoto:1997,
  author = {Akihiro Yamamoto},
  title = {Which Hypotheses Can Be Found with inverse Entailment?},
  editor = {Nada Lavra{\v{c}} and Sa{\v{s}}o D{\v{z}}eroski},
  booktitle = {Inductive Logic Programming. 7th International Workshop,
		  {ILP'97}, Prague, Czech Republic, Sept.\,17--20, 1997,
		  Proceedings},
  year = 1997,
  series = {Lecture Notes in Computer Science. Lecture Notes in
		  Artificial Intelligence},
  volume = 1297,
  pages = {296--308},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-63514-7},
  url = {http://www.springerlink.com/content/p368g628l6365821/},
  abstract = {In this paper we give a completeness theorem of an
		  inductive inference ruleinverse entailmentproposed by
		  Muggleton. Our main result is that a hypothesis clauseHcan
		  be derived from an exampleEunder a background theoryBwith
		  inverse entailment iffHsubsumesErelative toBin Plotkin's
		  sense. The theoryBcan be any clausal theory, and the
		  exampleEcan be any clause which is neither a tautology nor
		  implied byB. The derived hypothesisHis a clause which is
		  not always definite. In order to prove the result we give a
		  declarative semantics for arbitrary consistent clausal
		  theories, and show that SB-resolution, which was originally
		  introduced by Plotkin, is a complete procedural semantics.
		  The completeness is shown as an extension of the
		  completeness theorem of SLD-resolution. We also show that
		  every hypothesisHderived with saturant generalization,
		  proposed by Rouveirol, must subsume E w.r.t.Bin Buntine's
		  sense. Moreover we show that saturant generalization can be
		  obtained from inverse entailment by giving some restriction
		  to it.},
  doi = {10.1007/3540635149_58}
}
 
Qiang Yang, Rong Pan, and Sinno Jialin Pan. Learning recursive HTN-method structures for planning. In Proceedings of the Workshop on Artificial Intelligence Planning and Learning (Providence, Rhode Island, USA, Sept.22, 2007), 2007. In conjunction with the International Conference on Automated Planning and Scheduling (ICAPS'07).
@inproceedings{yang_ea:2007,
  author = {Qiang Yang and Rong Pan and Sinno Jialin Pan},
  title = {Learning Recursive {HTN}-Method Structures for Planning},
  booktitle = {Proceedings of the Workshop on Artificial Intelligence
		  Planning and Learning (Providence, Rhode Island, USA,
		  Sept.\,22, 2007)},
  year = 2007,
  note = {In conjunction with the International Conference on
		  Automated Planning and Scheduling ({ICAPS'07})},
  url = {http://www.cs.umd.edu/~ukuter/icaps07aipl/},
  keywords = {learning-and-planning planning read}
}
 
Serap Yilmaz. Inductive synthesis of recursive logic programs. Master's thesis, University of Bilkent, Computer Science Department, 1997.
@mastersthesis{yilmaz:1997,
  author = {Serap Yilmaz},
  title = {Inductive Synthesis of Recursive Logic Programs},
  school = {University of Bilkent, Computer Science Department},
  year = 1997,
  keywords = {Dialogs-II}
}
 
Tina Yu and Chris Clack. PolyGP: A polymorphic genetic programming system in haskell. In GP'98: Proceedings of the 3rd Conference on Genetic Programming (Madison, Wisconsin, July22-25, 1998), pages 416-427. Morgan Kaufmann, 1998. The annual GP conference is now part of the GECCO conference.
@inproceedings{yu/clack:1998,
  author = {Tina Yu and Chris Clack},
  title = {Poly{GP}: {A} Polymorphic Genetic Programming System in
		  Haskell},
  booktitle = {{GP'98}: Proceedings of the 3rd Conference on Genetic
		  Programming (Madison, Wisconsin, July\,22--25, 1998)},
  year = 1998,
  pages = {416--427},
  publisher = {Morgan Kaufmann},
  note = {The annual GP conference is now part of the {GECCO}
		  conference},
  documenturl = {http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/pgp.new.pdf},
  keywords = {PolyGP; enumerative ip; gp; higher-order functions;
		  induction; inductive programming; program evolution;
		  program synthesis},
  annote = {A genetic programming system in Haskell using higher-order
		  functions in order to evolve implicitly recursive
		  programs}
}
 
Tina Yu and Chris Clack. Recursion, lambda-abstractions and genetic programming. In Riccardo Poli, W. B. Langdon, Marc Schoenauer, Terry Fogarty, and Wolfgang Banzhaf, editors, EuroGP'98: Late Breaking Papers on the First European Workshop on Genetic Programming (Paris, France, April14-15, 1998, pages 26-30, 1998.
@inproceedings{yu/clack:1998b,
  author = {Tina Yu and Chris Clack},
  title = {Recursion, Lambda-Abstractions and Genetic Programming},
  editor = {Riccardo Poli and W. B. Langdon and Marc Schoenauer and
		  Terry Fogarty and Wolfgang Banzhaf},
  booktitle = {{EuroGP'98}: Late Breaking Papers on the First European
		  Workshop on Genetic Programming (Paris, France,
		  April\,14--15, 1998},
  year = 1998,
  pages = {26--30},
  keywords = {enumerative ip; gp; higher-order functions; ifp;
		  induction; inductive programming; program evolution;
		  program synthesis}
}
 
Tina Yu. An Analysis of the Impact of Functional Programming Techniques on Genetic Programming. PhD thesis, Department of Computer Science, University College London, 1999.
@phdthesis{yu:1999,
  author = {Tina Yu},
  title = {An Analysis of the Impact of Functional Programming
		  Techniques on Genetic Programming},
  school = {Department of Computer Science, University College
		  London},
  year = 1999,
  documenturl = {http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/Thesis.pdf},
  keywords = {enumerative ip; gp; higher-order functions; induction;
		  inductive programming; program evolution; program
		  synthesis}
}
 
Tina Yu. Hierarchical processing for evolving recursive and modular programs using higher-order functions and lambda abstraction. Genetic Programming and Evolvable Machines, 2(4):345-380, December 2001.
@article{yu:2001,
  author = {Tina Yu},
  title = {Hierarchical Processing for Evolving Recursive and Modular
		  Programs Using Higher-Order Functions and Lambda
		  Abstraction},
  journal = {Genetic Programming and Evolvable Machines},
  year = 2001,
  volume = 2,
  number = 4,
  pages = {345--380},
  month = {December},
  publisher = {Springer Netherlands},
  issn = {1389-2576 (Print) 1573-7632 (Online)},
  url = {http://www.springerlink.com/content/h540822u22541721/},
  doi = {10.1023/A:1012926821302},
  keywords = {enumerative ip; gp; higher-order functions; ifp;
		  induction; inductive programming; program evolution;
		  program synthesis},
  abstract = {We present a novel approach using higher-order functions
		  and lambda abstraction to evolve recursive and modular
		  programs. Moreover, a new term ``structure abstraction'' is
		  introduced to describe the property emerged from the
		  higher-order function program structure. We test this
		  technique on the general even-parity problem. The results
		  indicate that this approach is very effective with the
		  general even-parity problem due to the appropriate
		  selection of the foldr higher-order function. Initially,
		  foldr structure abstraction identify the promising area of
		  the search space at generation zero. Once the population is
		  within the promising area, foldr structure abstraction
		  provides hierarchical processing for search. Consequently,
		  solutions to the general even-parity problem are found very
		  efficiently. We identify the limitations of this new
		  approach and conclude that only when the appropriate
		  higher-order function is selected that the benefits of
		  structure abstraction show.}
}
 
Tina Yu. Polymorphism and genetic programming. In Genetic Programming. 4th European Conference, EuroGP'01, Lake Como, Italy, April18-20, 2001. Proceedings, volume 2038 of Lecture Notes in Computer Science, pages 218-233, Berlin/Heidelberg, 2001. Springer.
@inproceedings{yu:2001b,
  author = {Tina Yu},
  title = {Polymorphism and genetic programming},
  booktitle = {Genetic Programming. 4th European Conference, {EuroGP'01},
		  Lake Como, Italy, April\,18--20, 2001. Proceedings},
  year = 2001,
  series = {Lecture Notes in Computer Science},
  volume = 2038,
  pages = {218--233},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-41899-3},
  url = {http://www.springerlink.com/content/vx2bh2pt677k6bm0/},
  abstract = {Types have been introduced to Genetic Programming (GP) by
		  researchers with different motivation. We present the
		  concept of types in GP and introduce a typed GP system,
		  PolyGP, that supports polymorphism through the use of three
		  different kinds of type variable. We demonstrate the
		  usefulness of this kind of polymorphism in GP by evolving
		  two polymorphic programs (nth and map) using the system.
		  Based on the analysis of a series of experimental results,
		  we conclude that this implementation of polymorphism is
		  effective in assisting GP evolutionary search to generate
		  these two programs. PolyGP may enhance the applicability of
		  GP to a new class of problems that are difficult for other
		  polymorphic GP systems to solve.},
  doi = {10.1007/3-540-45355-5_17}
}
 
Tina Yu. A higher-order function approach to evolve recursive programs. In Tina Yu, Rick L. Riolo, and Bill Worzel, editors, Genetic Programming Theory and Practice III, volume 9 of Genetic Programming, chapter 7, pages 93-108. Springer, Ann Arbor, US, 12-14 May 2006.
@incollection{yu:2006,
  author = {Tina Yu},
  title = {A Higher-Order Function Approach to Evolve Recursive
		  Programs},
  editor = {Tina Yu and Rick L. Riolo and Bill Worzel},
  booktitle = {Genetic Programming Theory and Practice {III}},
  publisher = {Springer},
  year = 2006,
  volume = 9,
  series = {Genetic Programming},
  chapter = 7,
  pages = {93--108},
  address = {Ann Arbor, US},
  month = {12-14 May},
  issn = {1566-7863},
  isbn = {978-0-387-28110-0 (Print) 978-0-387-28111-7 (Online)},
  url = {http://www.springerlink.com/content/wh6jm67xpm1m1135/},
  doi = {10.1007/0-387-28111-8_7},
  abstract = {We demonstrate a functional style recursion implementation
		  to evolve recursive programs. This approach re-expresses a
		  recursive program using a non-recursive application of a
		  higher-order function. It divides a program recursion
		  pattern into two parts: the recursion code and the
		  application of the code. With the higher-order functions
		  handling recursion code application, GP effort becomes
		  focused on the generation of recursion code. We employed
		  this method to evolve two recursive programs: strstr C
		  library function and programs that produce the Fibonacci
		  sequence. In both cases, the program space defined by
		  higher-order functions are very easy for GP to find a
		  solution. We have learned about higher-order function
		  selection and fitness assignment through this study. The
		  next step will be to test the approach on applications with
		  open-ended solutions, such as evolutionary design.},
  keywords = {genetic algorithms; genetic programming; recursion;
		  Fibonacci sequence; strstr; PolyGP; type systems;
		  higher-order functions; recursion patterns; filter; foldr;
		  scanr; lambda abstraction; functional programming
		  languages; Haskell}
}
 
Chengqi Zhang, Hans W. Guesgen, and Wai-Kiang Yeap, editors. PRICAI'04: Trends in Artificial Intelligence. 8th Pacific Rim International Conference on Artificial Intelligence, Auckland, New Zealand, Aug.9-13, 2004. Proceedings, volume 3157 of Lecture Notes in Computer Science, Berlin/Heidelberg, 2004. Springer.
@proceedings{zhang_ea:2004,
  title = {{PRICAI'04}: Trends in Artificial Intelligence. 8th
		  Pacific Rim International Conference on Artificial
		  Intelligence, Auckland, New Zealand, Aug.\,9--13, 2004.
		  Proceedings},
  year = 2004,
  editor = {Chengqi Zhang and Hans W. Guesgen and Wai-Kiang Yeap},
  volume = 3157,
  series = {Lecture Notes in Computer Science},
  address = {Berlin\,/\,Heidelberg},
  publisher = {Springer},
  issn = {0302-9743 (Print) 1611-3349 (Online)},
  isbn = {978-3-540-22817-2},
  url = {http://springerlink.metapress.com/content/d27yf8bc7a64/},
  doi = {10.1007/b99563}
}

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