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    Efficient recognition of acyclic clustered constraint satisfaction problems

    Razgon, Igor and O'Sullivan, B. (2006) Efficient recognition of acyclic clustered constraint satisfaction problems. In: Azevedo, F. and Barahona, P. and Fages, F. and Rossi, F. (eds.) CSCLP 2006: Recent Advances in Constraints. Lecture Notes in Computer Science 4651. Springer, pp. 154-168. ISBN 9783540738169.

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    Abstract

    In this paper we present a novel approach to solving Constraint Satisfaction Problems whose constraint graphs are highly clustered and the graph of clusters is close to being acyclic. Such graphs are encountered in many real world application domains such as configuration, diagnosis, model-based reasoning and scheduling. We present a class of variable ordering heuristics that exploit the clustered structure of the constraint network to inform search. We show how these heuristics can be used in conjunction with nogood learning to develop efficient solvers that can exploit propagation based on either forward checking or maintaining arc-consistency algorithms. Experimental results show that maintaining arc-consistency alone is not competitive with our approach, even if nogood learning and a well known variable ordering are incorporated. It is only by using our cluster-based heuristics can large problems be solved efficiently. The poor performance of maintaining arc-consistency is somewhat surprising, but quite easy to explain.

    Metadata

    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 11 Oct 2021 11:42
    Last Modified: 11 Oct 2021 11:42
    URI: https://eprints.bbk.ac.uk/id/eprint/46241

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