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Learning cluster-based structure to solve constraint satisfaction problems

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Abstract

The hybrid search algorithm for constraint satisfaction problems described here first uses local search to detect crucial substructures and then applies that knowledge to solve the problem. This paper shows the difficulties encountered by traditional and state-of-the-art learning heuristics when these substructures are overlooked. It introduces a new algorithm, Foretell, to detect dense and tight substructures called clusters with local search. It also develops two ways to use clusters during global search: one supports variable-ordering heuristics and the other makes inferences adapted to them. Together they improve performance on both benchmark and real-world problems.

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Authors and Affiliations

  1. Department of Computer Science, The Graduate Center of The City University of New York, New York, NY, 10016, USA

    Xingjian Li & Susan L. Epstein

  2. Department of Computer Science, Hunter College of The City University of New York, New York, NY, 10065, USA

    Susan L. Epstein

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  1. Xingjian Li
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  2. Susan L. Epstein
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Correspondence to Xingjian Li.

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Li, X., Epstein, S.L. Learning cluster-based structure to solve constraint satisfaction problems. Ann Math Artif Intell 60, 91–117 (2010). https://doi.org/10.1007/s10472-010-9212-z

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