Abstract
Deduction and induction are unified on the basis of a generalized notion of logical consequence, having classical first-order logic as a particular case. RichProlog is a natural extension of Prolog rooted in this generalized logic, in the same way as Prolog is rooted in classical logic. Prolog can answer Σ 1 queries as a side effect of a deductive inference. RichProlog can answer Σ 1 queries, Π 1 queries (as a side effect of an inductive inference), and Σ 2 queries (as a side effect of an inductive inference followed by a deductive inference). RichProlog can be used to learn: a learning problem is expressed as a usual logic program, supplemented with data, and solved by asking a Σ 2 query. The output is correct in the limit, i.e., when sufficient data have been provided.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Angluin, D.: Finding patterns common to a set of strings. Journal of Computer and System Sciences. 21 (1980) 46–62
Angluin, D.: Inductive Inference of Formal Languages from Positive Data. Information and Control. 45 (1980) 117–135
Apt, K., Bol, R.: Logic Programming and Negation: A Survey. Journal of Logic Programming. 19/20 (1994) 177–190
Doets, K.: From Logic to Logic Programming. The MIT Press. (1994)
Jain, S., Osherson, D., Royer, J., Sharma, A. Systems that learn: An Introduction to Learning Theory, Second Edition. The MIT Press. (1999)
Kelly, K.: The Logic of Reliable Inquiry. Oxford University Press. (1996)
Le, T.: A general scheme for representing negative and quantified queries for deductive databases. Proceedings of the First Internatinal Conference on Information and Knowledge Management. Baltimore, Maryland. (1992)
Lifschitz, V.: Closed-World Databases and Circumscription. Artificial Intelligence. 27 (1985) 229–235
Martin, E., Osherson, D.: Elements of Scientific Inquiry. The MIT Press. (1998)
Martin, E., Sharma, A., Stephan, F.: A General Theory of Deduction, Induction, and Learning. In Jantke, K., Shinohara, A.: Proceedings of the Fourth International Conference on Discovery Science. Springer-Verlag. (2001) 228–242
Reiter, R.: On Closed-World Data Bases. In Gallaire, J., Minker, J., ed., Logic and Data Bases. Plenum Press. (1978) 55–76
Shepherdson, J.: Negation in Logic Programming. In Minker, J., ed., Foundations of Deductive databases and Logic Programming. Morgan Kaufmann. (1988) 19–88
Shoham, Y.: Reasoning about change. The MIT Press. (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martin, E., Nguyen, P., Sharma, A., Stephan, F. (2002). Learning in Logic with RichProlog. In: Stuckey, P.J. (eds) Logic Programming. ICLP 2002. Lecture Notes in Computer Science, vol 2401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45619-8_17
Download citation
DOI: https://doi.org/10.1007/3-540-45619-8_17
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43930-1
Online ISBN: 978-3-540-45619-3
eBook Packages: Springer Book Archive