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).
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