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• W2141063644 abstract "The ideas of intelligent backtracking (IB) and explanation-based learning (EBL) have developed independently in the constraint satisfaction, planning, machine learning and problem solving communities. The variety of approaches developed for IB and EBL in the various communities have hither-to been incomparable. 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 problems (CSPs). I show that both IB and EBL depend upon the common theory of explanation analysis—which involves explaining search failures, and regressing them to higher levels of the search tree. My comprehensive analysis shows that most of the differences between the CSP and planning approaches to EBL and IB revolve around different solutions to: (a) how the failure explanations are computed; (b) how they are contextualized (contextualization involves deciding whether or not to keep the flaw description and the description of the violated problem constraints); and (c) how the storage of explanations is managed. The differences themselves can be understood in terms of the differences between planning and CSP problems as instantiations of refinement search. This unified understanding is expected to support a greater cross-fertilization of ideas among CSP, planning and EBL communities." @default.
• W2141063644 created "2016-06-24" @default.
• W2141063644 creator A5023323170 @default.
• W2141063644 date "1998-10-01" @default.
• W2141063644 modified "2023-09-26" @default.
• W2141063644 title "On the relations between intelligent backtracking and failure-driven explanation-based learning in constraint satisfaction and planning" @default.
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• W2141063644 doi "https://doi.org/10.1016/s0004-3702(98)00087-3" @default.
• W2141063644 hasPublicationYear "1998" @default.
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