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A Model Seeker: Extracting Global Constraint Models from Positive Examples

  • Conference paper
Principles and Practice of Constraint Programming (CP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 7514))

Abstract

We describe a system which generates finite domain constraint models from positive example solutions, for highly structured problems. The system is based on the global constraint catalog, providing the library of constraints that can be used in modeling, and the Constraint Seeker tool, which finds a ranked list of matching constraints given one or more sample call patterns.

We have tested the modeler with 230 examples, ranging from 4 to 6,500 variables, using between 1 and 7,000 samples. These examples come from a variety of domains, including puzzles, sports-scheduling, packing & placement, and design theory. When comparing against manually specified “canonical” models for the examples, we achieve a hit rate of 50%, processing the complete benchmark set in less than one hour on a laptop. Surprisingly, in many cases the system finds usable candidate lists even when working with a single, positive example.

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Author information

Authors and Affiliations

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

    Nicolas Beldiceanu

  2. Cork Constraint Computation Centre, Department of Computer Science, University College Cork, Ireland

    Helmut Simonis

Authors
  1. Nicolas Beldiceanu
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  2. Helmut Simonis
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Editor information

Editors and Affiliations

  1. DEIS Universitá di Bologna, V.le Risorgimento, 2, 40136, Bologna, Italy

    Michela Milano

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Beldiceanu, N., Simonis, H. (2012). A Model Seeker: Extracting Global Constraint Models from Positive Examples. In: Milano, M. (eds) Principles and Practice of Constraint Programming. CP 2012. Lecture Notes in Computer Science, vol 7514. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33558-7_13

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  • DOI: https://doi.org/10.1007/978-3-642-33558-7_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33557-0

  • Online ISBN: 978-3-642-33558-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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