Abstract
We study the problem of inducing normal programs of multiple predicates in the empirical ILP setting. We identify a class of normal logic programs that can be handled and induced in a top-down manner by an intensional system. We propose an algorithm called NMPL that improves the multiple predicate learning system MPL and extends its language from definite to this class of normal programs. Finally, we discuss the cost of the MPL's refinement algorithm and present theoretical and experimental results showing that NMPL can be as effective as MPL and is computationally cheaper than it.
This work is part of the CNPq/ProTeM 3 project ICOM. The authors are partially supported by CAPES and CNPq, respectively.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Apt, K. R. and Bol, R. N., Logic Programming and Negation: A Survey, J. Logic Programming 19, 20: 9–71 (1994).
Bergadano, F., Gunetti, D., Nicosia, M. and Ruffo, G., Learning Logic Programs with Negation as Failure, in: S. Muggleton (ed.), Proceedings of the 5th International Workshop on Inductive Logic Programming, 1995.
Džeroski, S. and Bratko, I., Handling Noise in Inductive Logic Programming, in: S. Muggleton (ed.), Proceedings of the 2nd International Workshop on Inductive Logic Programming, 1992.
Flach, P. A.; A Framework for Inductive Logic Programming, in: S. Muggleton (ed.), Inductive Logic Programming, Academic Press, 1992, pp. 193–212.
Kunen, K., Signed Data Dependencies in Logic Programs, J. Logic Programming 7: 231–246 (1989).
Lavrač, N. and Dzeroski, S., Inductive Logic Programming: Techniques and Applications, Ellis Horwood, 1994.
Lloyd, J. W., Foundations of Logic Programming, 2nd edition, Springer, Berlin,1987.
Martin, L. and Vrain, C., MULTICN: an Empirical Multiple Predicate Learner, in: S. Muggleton (ed.), Proceedings of the 5th International Workshop on Inductive Logic Programming, 1995.
Muggleton, S. and De Raedt, L., Inductive Logic Programming: Theory and Methods, J. Logic Programming 19, 20: 629–679 (1994).
Plotkin, G. D., A Note on Inductive Generalization, in: B. Meltzer and D. Michie (eds.), Machine Intelligence 5, Elsevier North Holland, NY, 1970, pp. 153–163.
De Raedt, L., Lavrač, N. and Džeroski, S., Multiple Predicate Learning, in: Proceedings of the 13th International Joint Conference on Artificial Intelligence, Morgan Kaufman, 1993, pp. 1037–1042.
Sato, T., Completed Logic Programs and their Consistency, J. Logic Programming 9: 33–44 (1990).
Shapiro, E. Y., Algorithmic Program Debugging, MIT Press, 1983. *** DIRECT SUPPORT *** A0008D21 00006
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fogel, L., Zaverucha, G. (1998). Normal programs and multiple predicate learning. In: Page, D. (eds) Inductive Logic Programming. ILP 1998. Lecture Notes in Computer Science, vol 1446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0027321
Download citation
DOI: https://doi.org/10.1007/BFb0027321
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64738-6
Online ISBN: 978-3-540-69059-7
eBook Packages: Springer Book Archive