Explanation-based generalisation = partial evaluation
Artificial Intelligence
Inferring decision trees using the minimum description length principle
Information and Computation
Explanation-based learning: a problem solving perspective
Artificial Intelligence
Learning approximate control rules of high utility
Proceedings of the seventh international conference (1990) on Machine learning
Integrated learning in a real domain
Proceedings of the seventh international conference (1990) on Machine learning
The Utility of Knowledge in Inductive Learning
Machine Learning
Compiling prior knowledge into an explicit basis
ML92 Proceedings of the ninth international workshop on Machine learning
Refining a relational theory with multiple faults in the concept and subconcepts
ML92 Proceedings of the ninth international workshop on Machine learning
Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Investigating Explanation-Based Learning
Investigating Explanation-Based Learning
PROLOG Programming for Artificial Intelligence
PROLOG Programming for Artificial Intelligence
Algorithmic Program DeBugging
Learning Logical Definitions from Relations
Machine Learning
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Inductive generalization of analytically learned goal hierarchies
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
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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).