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  Combining FOIL and EBG to speed-up logic programs (1993) [32 citations — 6 self]

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by John M. Zelle, Raymond J. Mooney
In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence
ftp://ftp.cs.utexas.edu/pub/mooney/papers/dolphin-ijcai-93.ps.Z
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Abstract:

This paper presents an algorithm that combines traditional EBL techniques and recent developments in inductive logic programming to learn effective clause selection rules for Prolog programs. When these control rules are incorporated into the original program, significant speed-up may be achieved. The algorithm is shown to be an improvement over competing EBL approaches in several domains. Additionally, the algorithm is capable of automatically transforming some intractable algorithms into ones that run in polynomial time.

Citations

747 Learning logical definitions from relations – Quinlan - 1990
471 Explanationbased generalization: a unifying view – Mitchell, Keller, et al. - 1986
331 Explanation-based learning: an alternative view – Dejong, Mooney - 1986
279 Quantitative results concerning the utility of explanation-based learning – Minton - 1988
230 Chunking in Soar: The anatomy of a general learning mechanism – Laird, Rosenbloom, et al. - 1986
111 Prolog Programming for Artificial Intelligence – Bratko - 1990
63 Learning at the Knowledge Level – Dietterich - 1986
43 Learning approximate control rules of high utility – Cohen - 1990
33 Learning to search: From weak methods to domain-specific heuristics – Langley - 1985
33 PROLEARN: towards a prolog interpreter that learns – Prieditis, Mostow - 1987
22 The utility of background knowledge in inductive learning – Pazzani, Kibler - 1992
20 The role of explicit contextual knowledge in learning concepts to improve performance – Keller - 1987
14 Approximating learned search control knowledge – Chase, Zweben, et al. - 1989
13 Automated synthesis of constrained generators – Braudaway, Tong - 1989
2 De Schreye, and Bruno Krekels. Compiling control – Bruynooghe, Danny - 1989