Discovering problem solving strategies: what humans do and machines don't (yet)
Proceedings of the sixth international workshop on Machine learning
A Critical Look at Experimental Evaluations of EBL
Machine Learning
Automatically generating abstractions for problem solving
Automatically generating abstractions for problem solving
A structural theory of explanation-based learning
A structural theory of explanation-based learning
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
Using and refining simplifications: explanation-based learning of plans in intractable domains
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Abstraction in problem solving and learning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Lazy explanation-based learning: a solution to the intractable theory problem
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
A bayesian approach to tackling hard computational problems
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Discovering hidden structure in factored MDPs
Artificial Intelligence
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This paper describes the integration of abstraction and explanation-based learning (EBL) in the context of the PRODIGY system. PRODIGY'S abstraction module creates a hierarchy of abstract problem spaces, so problem solving can proceed in a more directed fashion. The EBL module acquires search control knowledge by analyzing problemsolving traces. When the two modules are integrated, they tend to complement each other's capabilities, resulting in performance improvements that neither system can achieve independently. We present empirical results showing the effect of combining the two modules and describe the factors that influence the overall performance of the integrated system.