Production system models of learning and development
The role of explicit contextual knowledge in learning concepts to improve performance
The role of explicit contextual knowledge in learning concepts to improve performance
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
RESIDUE: a deductive approach to design synthesis
RESIDUE: a deductive approach to design synthesis
The compleat guide to MRS
Probabilistic Explanation Based Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Operationality criteria for recursive predicates
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
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This paper describes a domain-independent implementation of explanation-based generalization (EBG) within a logic-programming environment. Explanation is interleaved with generalization, so that as the training instance is proven to be a positive example of the goal concept, the generalization is simultaneously created. All aspects of the EBG task are viewed in logic, which provides a clear semantics for EBG, and allows its integration into the logic-programming system. In this light operationally becomes a property requiring explicit reasoning. Additionally, viewing EBG in logic clarifies the relation of learning search-control to EBG, and suggests solutions for dealing with imperfect domain theories.