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Adapting Consistency in Constraint Solving

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Data Mining and Constraint Programming

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10101))

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Abstract

State-of-the-art constraint solvers uniformly maintain the same level of local consistency (usually arc consistency) on all the instances. We propose two approaches to adjust the level of consistency depending on the instance and on which part of the instance we propagate. The first approach, parameterized local consistency, uses as parameter the stability of values, which is a feature computed by arc consistency algorithms during their execution. Parameterized local consistencies choose to enforce arc consistency or a higher level of local consistency to a value depending on whether the stability of the value is above or below a given threshold. In the adaptive version, the parameter is dynamically adapted during search, and so is the level of local consistency. In the second approach, we focus on partition-one-AC, a singleton-based consistency. We propose adaptive variants of partition-one-AC that do not necessarily run until having proved the fixpoint. The pruning can be weaker than the full version, but the computational effort can be significantly reduced. Our experiments show that adaptive parameterized maxRPC and adaptive partition-one-AC can obtain significant speed-ups over arc consistency and over the full versions of maxRPC and partition-one-AC.

The results contained in this chapter have been presented in [BBCB13] and [BBBT14]. This work has been funded by the EU project ICON (FP7-284715).

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Notes

  1. 1.

    http://cpai.ucc.ie/09/.

  2. 2.

    www.cril.univ-artois.fr/~lecoutre/benchmarks.html.

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Authors and Affiliations

  1. TASC (INRIA/CNRS), Mines Nantes, Nantes, France

    Amine Balafrej

  2. CNRS, University of Montpellier, Montpellier, France

    Christian Bessiere, Anastasia Paparrizou & Gilles Trombettoni

Authors
  1. Amine Balafrej
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  2. Christian Bessiere
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  3. Anastasia Paparrizou
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  4. Gilles Trombettoni
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Corresponding author

Correspondence to Amine Balafrej .

Editor information

Editors and Affiliations

  1. Université Montpellier 2, Montpellier, France

    Christian Bessiere

  2. KU Leuven, Heverlee, Belgium

    Luc De Raedt

  3. University of British Columbia, Vancouver, British Columbia, Canada

    Lars Kotthoff

  4. Université Catholique de Louvain, Louvain-la-Neuve, Belgium

    Siegfried Nijssen

  5. University College Cork, Cork, Ireland

    Barry O'Sullivan

  6. University of Pisa, Pisa, Italy

    Dino Pedreschi

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Balafrej, A., Bessiere, C., Paparrizou, A., Trombettoni, G. (2016). Adapting Consistency in Constraint Solving. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., Pedreschi, D. (eds) Data Mining and Constraint Programming. Lecture Notes in Computer Science(), vol 10101. Springer, Cham. https://doi.org/10.1007/978-3-319-50137-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-50137-6_9

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  • Online ISBN: 978-3-319-50137-6

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