Abstract
In this paper we present a framework for learning non-monotonic logic programs. The method is parametric on a classical learning algorithm whose generated rules are to be understood as default rules. This means that these rules must be tolerant to the negative information by allowing for the possibility of exceptions. The same classical algorithm is then used to learn recursively these exceptions.
We prove that the non-monotonic learning algorithm that realizes these ideas converges asymptotically to the concept to be learned. We also discuss various general issues concerning the problem of learning nonmonotonic theories in the proposed framework.
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M. Bain and S. Muggleton, Non-monotonic learning. In: J.E. Hayes-Michie and E. Tyugu, eds., Machine Intelligence 12. Oxford University Press, 1990
K.L. Clark. Negation as failure. In Logic and databases, Gallaire and Minker, eds., Plenum Press, 1978.
J. Cussens, A. Hunter and A. Srinivasan. Generating explicit ordering for nonmonotonic logics. Proc. of AAAI-93.
L. De Raedt. Interactive Theory Revision: an Inductive Logic Programming Approach. Academic Press, 1992.
L. De Raedt and M. Bruynooghe. On negation and three-valued logic in interactive concept learning. Proc. of the 9th European Conference on AI, ECAI-90, 207–212, 1990.
M. Gelfond and V. Lifschitz. The stable model semantics for logic programs. Proc. of the 5th International Conference and Symposium on Logic Programming, 1070–1080, MIT Press, 1990.
E.M. Gold. Language identification in the limit. Information and Control, 10:447–474, 1967.
A. Kakas, P. Mancarela and P. M. Dung. The acceptability semantics for logic programs. Proc. of 11th Inter. Conference on Logic Programming, ICLP-94, 504–519, MIT Press, 1994.
J-U. Kietz and S. Dzeroski. Inductive logic programming and learnability. SIGART Newsletters, 5(1), 1994.
N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.
C. Ling. Non-Monotonic specialization. Proc. of the Inductive Logic Programming Workshop, ILP-91, 1991.
S. Muggleton and W. Buntime. Machine invention of first order predicates by inverting resolution. Proc. of the 5th Inter. Conference on Machine Learning, 339–352, Kaufmann, 1988.
S. Muggleton. Inductive logic programming. New Generation Computing, 8, 295–318, 1991.
S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. submitted.
T. Przymusinski, On the declarative and procedural semantics of logic programs. Journal of Automated Reasoning, 5, 167–205, 1989.
A. Srinivasan, S. Muggleton and M. Bain. Distinguishing exceptions from noise in non-monotonic learning. Proc. of the International Workshop on Inductive Logic Programming, S. Muggleton and K. Furukawa, Japan, 1992.
S. Wrobel. On the proper definition of minimality in specialization and theory revision. Proc. of the European Conference on Machine Learning, ECML-93, Vienna, 1993, LNAI 667, Springer Verlag.
A. Van Gelder, K. A. Ross and J. S. Schlipf. Unfounded sets and well-founded semantics for general logic programs. Proc. of the 7th Symposium on Principles of Database Systems, PODS-88, 221–230, ACM Press, 1988.
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Dimopoulos, Y., Kakas, A. (1995). Learning non-monotonic logic programs: Learning exceptions. In: Lavrac, N., Wrobel, S. (eds) Machine Learning: ECML-95. ECML 1995. Lecture Notes in Computer Science, vol 912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59286-5_53
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DOI: https://doi.org/10.1007/3-540-59286-5_53
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