Integrating ILP and EBL R a y m o n d J. Mooney and J o h n M. Zelle D e p a r t m e n t of C o m p u t e r Sciences University of Texas Austin, T X 78712 mooney@cs.utexas.edu, zelle@cs.utexas.edu Abstract This paper presents a review of recent work that integrates methods from Inductive Logic Programming (ILP) and Explanation-Based Learning (EBL). ILP and EBL methods have complementary strengths and weaknesses and a number of recent projects have effectively combined them into systems with better performance than either of the individual approaches. In particular, integrated systems have been developed for guiding induction with prior knowledge (ML-Smart, FOCL, GRENDEL) refining imperfect domain theories (FORTE, AUDREY, Rx), and learning effective search-control knowledge (AxA-EBL, DOLPHIN). incorrect domain theory. Two general approaches to integrating ILP and EBL in concept learning have been explored. One approach, knowledge-guided induction, uses the existing domain theory to guide the learning of a separate concept definition, e.g. [2, 34, 7]. The other approach, theory refinement, uses the examples to modify the existing domain theory in an attempt to improve its accuracy, e.g. [39, 52, 47]. I L P /
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Integrating ILP and EBL
Mooney, Raymond J.; Zelle, John M.
Follow ACM SIGART Bulletin
, Volume 5 (1)
Association for Computing Machinery – Jan 1, 1994
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