Abstract
This paper studies equivalence issues in inductive logic programming. A background theory B 1 is inductively equivalent to another background theory B 2 if B 1 and B 2 induce the same hypotheses for any given set of examples. Inductive equivalence is useful to compare inductive capabilities among agents having different background theories. Moreover, it provides conditions for optimizing background theories through appropriate program transformations. In this paper, we consider three different classes of background theories: clausal theories, Horn logic programs, and nonmonotonic extended logic programs. We show that logical equivalence is the necessary and sufficient condition for inductive equivalence in clausal theories and Horn logic programs. In nonmonotonic extended logic programs, on the other hand, strong equivalence is necessary and sufficient for inductive equivalence in general. Interestingly, however, we observe that several existing induction algorithms require weaker conditions of equivalence under restricted problem settings. We also discuss connection to equivalence in abductive logic and conclude that the notion of strong equivalence is useful to characterize equivalence of non-deductive reasoning.
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Baral, C., Gelfond, M.: Logic programming and knowledge representation. Journal of Logic Programming 19/20, 73–148 (1994)
De Raedt, L.: Logical settings for concept-learning. Artificial Intelligence 95, 187–201 (1997)
De Raedt, L., Dehaspe, L.: Clausal discovery. Machine Learning 26, 99–146 (1997)
Denecker, M., Kakas, A.: Abductive logic programming. In: Kakas, A.C., Sadri, F. (eds.) Computational Logic: Logic Programming and Beyond. LNCS (LNAI), vol. 2407, pp. 402–436. Springer, Heidelberg (2002)
Eiter, T., Fink, M.: Uniform equivalence of logic programs under the stable model semantics. In: Palamidessi, C. (ed.) ICLP 2003. LNCS, vol. 2916, pp. 224–238. Springer, Heidelberg (2003)
Flach, P.A., Kakas, A.C. (eds.): Abduction and Induction — Essays on their Relation and Integration. Kluwer Academic, Dordrecht (2000)
Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proceedings of the 5th International Conference and Symposium on Logic Programming, pp. 1070–1080. MIT Press, Cambridge (1988)
Gelfond, M., Lifschitz, V.: Logic programs with classical negation. In: Proceedings of the 7th International Conference on Logic Programming, pp. 579–597. MIT Press, Cambridge (1990)
Inoue, K.: Induction as consequent finding. Machine Learning 55, 109–135 (2004)
Inoue, K., Sakama, C.: Equivalence of logic programs under updates. In: Alferes, J.J., Leite, J. (eds.) JELIA 2004. LNCS (LNAI), vol. 3229, pp. 174–186. Springer, Heidelberg (2004)
Inoue, K., Sakama, C.: Equivalence in abductive logic. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (2005) (to appear)
Janhunen, T., Oikarinen, E.: LPEQ and DLPEQ – translators for automated equivalence testing of logic programs. In: Lifschitz, V., Niemelä, I. (eds.) LPNMR 2004. LNCS (LNAI), vol. 2923, pp. 336–340. Springer, Heidelberg (2003)
Lifschitz, V., Pearce, D., Valverde, A.: Strongly equivalent logic programs. ACM Transactions on Computational Logic 2, 526–541 (2001)
Lin, F.: Reducing strong equivalence of logic programs to entailment in classical propositional logic. In: Proceedings of the 8th International Conference on Principles of Knowledge Representation and Reasoning, pp. 170–176. Morgan Kaufmann, San Francisco (2002)
Maher, M.J.: Equivalence of logic programs. In: Minker, J. (ed.) Foundations of Deductive Databases and Logic Programming, pp. 627–658. Morgan Kaufmann, San Francisco (1988)
McCarthy, J.: Circumscription – a form of nonmonotonic reasoning. Artificial Intelligence 13(1&2), 27–39 (1980)
Minker, J.: On indefinite data bases and the closed world assumption. In: Loveland, D.W. (ed.) CADE 1982. LNCS, vol. 138, pp. 292–308. Springer, Heidelberg (1982)
Muggleton, S., Feng, C.: Efficient induction algorithm. In: [19], pp. 281–298 (1992)
Muggleton, S. (ed.): Inductive Logic Programming. Academic Press, London (1992)
Muggleton, S.: Inverse entailment and Progol. New Generation Computing 13, 245–286 (1995)
Nienhuys-Cheng, S.-H., de Wolf, R.: Foundations of Inductive Logic Programming. LNCS, vol. 1228. Springer, Heidelberg (1997)
Otero, R.P.: Induction of stable models. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, pp. 193–205. Springer, Heidelberg (2001)
Osorio, M., Navarro, J.A., Arrazola, J.: Equivalence in answer set programming. In: Pettorossi, A. (ed.) LOPSTR 2001. LNCS, vol. 2372, pp. 57–75. Springer, Heidelberg (2002)
Plotkin, G.D.: A further note on inductive generalization. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence, vol. 6, pp. 101–124. Edinburgh University Press (1971)
Sagiv, Y.: Optimizing Datalog programs. In: Minker, J. (ed.) Foundations of Deductive Databases and Logic Programming, pp. 659–668. Morgan Kaufmann, San Francisco (1988)
Sakama, C.: Induction from answer sets in nonmonotonic logic programs. ACM Transactions on Computational Logic, 6(2):203–231(2005); Preliminary version: Learning by answer sets. In: Proceedings of the AAAI Spring Symposium on Answer Set Programming, pp. 181–187. AAAI Press, Menlo Park (2001)
Yamamoto, A.: Which hypotheses can be found with inverse entailment? In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 296–308. Springer, Heidelberg (1997)
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Sakama, C., Inoue, K. (2005). Inductive Equivalence of Logic Programs. In: Kramer, S., Pfahringer, B. (eds) Inductive Logic Programming. ILP 2005. Lecture Notes in Computer Science(), vol 3625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536314_19
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DOI: https://doi.org/10.1007/11536314_19
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