Specific Filtering Algorithms for Over-Constrained Problems

  • Thierry Petit
  • Jean-Charles Régin
  • Christian Bessière
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2239)


In recent years, many constraint-specific filtering algorithms have been introduced. Such algorithms use the semantics of the constraint to perform filtering more efficiently than a generic algorithm. The usefulness of such methods has been widely proven for solving constraint satisfaction problems. In this paper, we extend this concept to overconstrained problems by associating specific filtering algorithms with constraints that may be violated. We present a paradigm that places no restrictions on the constraint filtering algorithms used. We illustrate our method with a complete study of the All-different constraint.


Constraint Satisfaction Problem Primal Graph Filter Algorithm Deletion Condition Disjunctive Constraint 
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  1. 1.
    R. Ahuja, T. Magnanti, and J. Orlin. Networks flows: theory, algorithms, and applications. Prentice Hall, Inc., 1993.Google Scholar
  2. 2.
    N. Beldiceanu and E. Contejean. Introducing global constraints in chip. Journal of Mathematical and Computer Modelling, 20(12):97–123, 1994.zbMATHCrossRefGoogle Scholar
  3. 3.
    S. Bistarelli, U. Montanari, F. Rossi, T. Schiex, G. Verfaillie, and H. Fargier. Semiring-based CSPs and valued CSPs: Frameworks, properties, and comparison. Constraints, 4:199–240, 1999.zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    R. Dechter. Constraint networks. Encyclopedia of Artificial Intelligence, pages 276–285, 1992.Google Scholar
  5. 5.
    L. Ford and D. Flukerson. Flows in networks. Princeton University Press, 1962.Google Scholar
  6. 6.
    E. Freuder and R. Wallace. Partial constraint satisfaction. Artificial Intelligence, 58:21–70, 1992.CrossRefMathSciNetGoogle Scholar
  7. 7.
    I. Gent, K. Stergiou, and T. Walsh. Decomposable constraints. Artificial Intelligence, 123:133–156, 2000.zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    J. Larrosa, P. Meseguer, and T. Schiex. Maintaining reversible DAC for MAX-CSP. Artificial Intelligence, 107:149–163, 1999.zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    T. Petit, J.-C. Régin, and C. Bessière. Meta constraints on violations for over constrained problems. Proceedings ICTAI, pages 358–365, 2000.Google Scholar
  10. 10.
    J.-C. Régin. A filtering algorithm for constraints of difference in CSPs. Proceedings AAAI, pages 362–367, 1994.Google Scholar
  11. 11.
    J.-C. Régin. Développement d’outils algorithmiques pour l’Intelligence Artificielle. Application á la Chimie Organique. Ph.D. Dissertation, Université Montpellier II, 1995.Google Scholar
  12. 12.
    J.-C. Régin. Generalized arc consistency for global cardinality constraint. Proceedings AAAI, pages 209–215, 1996.Google Scholar
  13. 13.
    J.-C. Régin, T. Petit, C. Bessiére, and J.-F. Puget. An original constraint based approach for solving over constrained prolems. Proceedings CP, pages 543–548, 2000.Google Scholar
  14. 14.
    T. Schiex. Arc consistency for soft constraints. Proceedings CP, pages 411–424, 2000.Google Scholar
  15. 15.
    G. Verfaillie, M. Lemaître, and T. Schiex. Russian doll search for solving constraint optimisation problems. Proceedings AAAI, pages 181–187, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Thierry Petit
    • 1
    • 2
  • Jean-Charles Régin
    • 1
  • Christian Bessière
    • 2
  1. 1.ILOGValbonneFRANCE
  2. 2.LIRMM (UMR 5506 CNRS)Montpellier Cedex 5FRANCE

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