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Continuous Search in Constraint Programming

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Autonomous Search

Abstract

This work presents the concept of Continuous Search (CS), whose objective is to allow any user to eventually get their constraint solver achieving a top performance on their problems. Continuous Search comes in two modes: the functioning mode solves the user’s problem instances using the current heuristics model; the exploration mode reuses these instances to train and improve the heuristics model through Machine Learning during the computer idle time. Contrasting with previous approaches, Continuous Search thus does not require that the representative instances needed to train a good heuristics model be available beforehand. It achieves lifelong learning, gradually becoming an expert on the user’s problem instance distribution. Experimental validation suggests that Continuous Search can design efficient mixed strategies after considering a moderate number of problem instances.

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Author information

Authors and Affiliations

  1. Microsoft-INRIA joint lab, Orsay, France

    Alejandro Arbelaez

Authors
  1. Alejandro Arbelaez
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  2. Youssef Hamadi
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  3. Michèle Sebag
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Corresponding author

Correspondence to Alejandro Arbelaez .

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Editors and Affiliations

  1. Microsoft Research Cambridge, JJ Thomson Avenue 7, Cambridge, CB3 0FB, United Kingdom

    Youssef Hamadi

  2. Federico Santa María, Departamento de Informática, Universidad Técnica, Avenida España 1680, Valparaíso, Chile

    Eric Monfroy

  3. Faculté des Sciences, LERIA, Université d'Angers, Boulevard Lavoisier 2, Angers CX 01, 49045, France

    Frédéric Saubion

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Arbelaez, A., Hamadi, Y., Sebag, M. (2011). Continuous Search in Constraint Programming. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds) Autonomous Search. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21434-9_9

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  • DOI: https://doi.org/10.1007/978-3-642-21434-9_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21433-2

  • Online ISBN: 978-3-642-21434-9

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