Abstract
Some inductive logic programming (ILP) systems use determinate literals to efficiently induce logic programs. A determinate literal literal is a literal that does not distinguish positive examples from negative examples, but produces information in variables introduced by the literal. The concept of determinate literals, however, is not reflected by the concept of input/output mode of predicate attributes properly, and so a system using determinate literals may induce inconsistent logic programs with predicate mode or inexecutable programs. The paper extends the concept of determinate literals and proposes input and output determinate literals. These literals function as pre-processor and post-processor against other literals. The paper also describes an implementation of the method and experimentations.
Preview
Unable to display preview. Download preview PDF.
References
David W. Aha, Stephen Lapointe, Charles X. Ling, and Stan Matwin, “Inverting implication with small training sets”, In Proc. of 7th European Conf. on Machine Learning, LNAI, 784, Springer-Verlag, pp 31–48, 1994.
David W. Aha, Stephane Lapointe, Charles X. Ling, and Stan Matwin, “Learning recursive relations with randomly selected small training sets”, In Proc. of 11th Int'l Conf. on Machine Learning, pp 12–18. Morgan Kaufmann, 1994.
Francesco Bergadano and Daniele Gunetti. “Inductive Logic Programming — From Machine Learning to Software Engineering”, MIT Press, 1996.
Mitsue Furusawa, Nobuhiro Inuzuka, Hirohisa Seki and Hidenori Itch. “Bottom-up induction of logic programs with more than one recursive clause” to appear in Proc. IJCAI97 Workshop Frontiers of Inductive Logic Programming, Nagoya, 1997.
Mitsue Furusawa, Nobuhiro Inuzuka, Hirohisa Seki and Hidenori Itoh. “Induction of logic programs with more than one recursive clause by analyzing saturations”, to appear in Proceedings of 7th Int'1 Inductive Logic Programming Workshop (ILP97), LNAI seriese, Springer Verlag, Prague, 1997.
Peter Idestam-Almquist. “Efficient Induction of Recursive Definitions by Structural Analysis of Saturations”, Proc. of 5th Int'l Workshop on Inductive Logic Programming, pp 77–94, 1995.
Nobuhiro Inuzuka, Masakage Kamo, Naohiro Ishii, Hirohisa Seki and Hidenori Itoh. “Top-down Induction of Logic Programs from Incomplete Samples”, In Proc. 6th Int'l Inductive Logic Programming Workshop, pp 119–136, 1996.
Stephan Lapointe and Stan Matwin, “Sub-unification: a tool for efficient induction of recursive programs”, In Proc. of 9th Int'l Conf. on Machine Learning, Aberdeen, Morgan Kaufmann, pp 273–281, 1992.
John W. Lloyd. “Foundation of Logic Programming”, Second, Extended Edition, Springer-Verlag, 1993.
Stephen Muggleton, “Inverse entailment and progol”, New Generation Computing, 3+4 pp 245–286, 1995.
Stephen Muggleton and Cao Feng, “EfFicient. induction in logic programs”, In S. Muggleton, editor, Inductive Logic Programming, pp 281–298, Academic Press, 1992.
J.R. Quinlan. Learning logical definitions from relations. Machine Learning, 5, pp. 239–266,1990.
J.R. Quinlan and R.M. Cameron-Jones. “FOIL: A midterm report”, In P. Brazdil, editor, Proc. 6th European Conf. Machine Learning, LNAI, 667, Springer-Verlag, pp 3–20, 1993.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Inuzuka, N., Seki, H., Itoh, H. (1997). Efficient induction of executable logic programs from examples. In: Shyamasundar, R.K., Ueda, K. (eds) Advances in Computing Science — ASIAN'97. ASIAN 1997. Lecture Notes in Computer Science, vol 1345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63875-X_54
Download citation
DOI: https://doi.org/10.1007/3-540-63875-X_54
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63875-9
Online ISBN: 978-3-540-69658-2
eBook Packages: Springer Book Archive