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Boosting Distributed Constraint Networks

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Combinatorial Search: From Algorithms to Systems

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Abstract

Competition and cooperation can boost the performance of a combinatorial search process. Both can be implemented with a portfolio of algorithms which run in parallel, give hints to each other and compete for being the first to finish and deliver the solution. In this chapter we present a new generic framework for the application of algorithms for distributed constraint satisfaction that makes use of both cooperation and competition. This framework improves the performance of two different standard algorithms by one order of magnitude. Furthermore, it can reduce the risk of poor performance by up to three orders of magnitude diminishing the heavy-tailed behaviour of complete distributed search.

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Notes

  1. 1.

    We decided to use the median instead of the mean to alleviate the effects of messages interleaving. Indeed, interleaving can give disparate measures which can be pruned by the median calculation.

  2. 2.

    We made preliminary experiments to determine this.

  3. 3.

    Bookkeeping could definitely help to reduce the amount of constraint checks in the computation of maxSupport.

  4. 4.

    We decided to use this method since it was shown to minimize nccc on previous tests (see Table 2.2).

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Authors and Affiliations

  1. Microsoft Research Cambridge, Cambridge, UK

    Youssef Hamadi

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  1. Youssef Hamadi
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Hamadi, Y. (2013). Boosting Distributed Constraint Networks. In: Combinatorial Search: From Algorithms to Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41482-4_2

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  • DOI: https://doi.org/10.1007/978-3-642-41482-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41481-7

  • Online ISBN: 978-3-642-41482-4

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