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Learning Parameters for the Sequence Constraint from Solutions

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Principles and Practice of Constraint Programming (CP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9892))

Abstract

This paper studies the problem of learning parameters for global constraints such as Sequence from a small set of positive examples. The proposed technique computes the probability of observing a given constraint in a random solution. This probability is used to select the more likely constraint in a list of candidates. The learning method can be applied to both soft and hard constraints.

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Notes

  1. 1.

    The benchmark is available upon request to the authors.

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Author information

Authors and Affiliations

  1. Université Laval, 2325, Rue de L’Université, Québec, G1V 0A6, Canada

    Émilie Picard-Cantin & Claude-Guy Quimper

  2. PetalMD, 350 Boulevard Charest Est, Québec, G1K 3H5, Canada

    Mathieu Bouchard & Jason Sweeney

Authors
  1. Émilie Picard-Cantin
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  2. Mathieu Bouchard
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  3. Claude-Guy Quimper
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  4. Jason Sweeney
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Corresponding author

Correspondence to Émilie Picard-Cantin .

Editor information

Editors and Affiliations

  1. University of Nice Sophia Antipolis , Sophia Antipolis, France

    Michel Rueher

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Picard-Cantin, É., Bouchard, M., Quimper, CG., Sweeney, J. (2016). Learning Parameters for the Sequence Constraint from Solutions. In: Rueher, M. (eds) Principles and Practice of Constraint Programming. CP 2016. Lecture Notes in Computer Science(), vol 9892. Springer, Cham. https://doi.org/10.1007/978-3-319-44953-1_26

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  • DOI: https://doi.org/10.1007/978-3-319-44953-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44952-4

  • Online ISBN: 978-3-319-44953-1

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