Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Principles of artificial intelligence
Principles of artificial intelligence
Explanation-based learning: a problem solving perspective
Artificial Intelligence
Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On-Line Learning from Search Failures
Machine Learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Artificial Intelligence - Special volume on planning and scheduling
Failure driven dynamic search control for partial order planners: an explanation based approach
Artificial Intelligence
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The ideas of dependency directed backtracking (DDB) and explanation based learning (EBL) have developed independently in constraint satisfaction. planning and problem solving communities. In this paper. I formalize and unify these ideas under the task-independent framework of refinement search. which can model the search strategies used in both planning and constraint satisfaction. I show that both DDB and EBL depend upon the common theory of explaining search failures and regressing them to higher levels of the search tree. The relevant issues of importance include (a) how the failures are explained and (b) how many failure explanations are remembered. This task-independent understanding of DDB and EBL helps support cross-fertilization of ideas among Constraint Satisfaction. Planning and Explanation-Based Learning communities.