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Reasoning about Constraint Models

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PRICAI 2014: Trends in Artificial Intelligence (PRICAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8862))

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Abstract

We propose a simple but powerful framework for reasoning about properties of models specified in languages like AMPL, OPL, Zinc or Essence. Using this framework, we prove that reasoning problems like detecting symmetries, redundant constraints or dualities between models are undecidable even for a very limited modelling language that only generates simple problem instances. To provide tools to help the human modeller (for example, to identify when a model has a particular symmetry), it would nevertheless be useful to automate many of these reasoning tasks. To explore the possibility of doing this, we describe two case-studies. The first uses the ACL2 inductive prover to prove inductively that a model contains a symmetry. The second identifies a tractable fragment of MiniZinc and uses a decision procedure to prove that a model implies a parameterized constraint.

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Author information

Authors and Affiliations

  1. CNRS and University of Montpellier, Montpellier, France

    Christian Bessiere

  2. LAAS-CNRS, Toulouse, France

    Emmanuel Hebrard

  3. INRA Toulouse, Toulouse, France

    George Katsirelos

  4. University of Bologna, Bologna, Italy

    Zeynep Kiziltan

  5. University of Toronto, Toronto, Canada

    Nina Narodytska

  6. NICTA and UNSW, Sydney, Australia

    Toby Walsh

Authors
  1. Christian Bessiere
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  2. Emmanuel Hebrard
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  3. George Katsirelos
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  4. Zeynep Kiziltan
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  5. Nina Narodytska
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  6. Toby Walsh
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Editor information

Editors and Affiliations

  1. MIMOS Berhad Technology Park Malaysia, 57000, Bukit Jalil, KL, Malaysia

    Duc-Nghia Pham

  2. Kyungpook National University, Sankyuk-Dong, Buk-Gu, 702-701, Daegu, Korea

    Seong-Bae Park

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© 2014 Springer International Publishing Switzerland

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Bessiere, C., Hebrard, E., Katsirelos, G., Kiziltan, Z., Narodytska, N., Walsh, T. (2014). Reasoning about Constraint Models. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_63

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  • DOI: https://doi.org/10.1007/978-3-319-13560-1_63

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13559-5

  • Online ISBN: 978-3-319-13560-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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