A distributed approach to partial constraint satisfaction problems

  • Khaled Ghedira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1069)

Abstract

First we present, in this paper, a multi-agent approach for the partial constraint satisfaction of overconstrained problems. The approach comes from the Eco-problem solving ideas based on interactions between agents, each of them trying to reach its own satisfaction. It works by displacements within the set of possible states searching for a state satisfying the greatest number of constraints. These displacements are guided by stochastic and heuristic local repairs distributed on each variable. Each variable performs its repairs by using its own simulated annealing process combined with a min-conflicts heuristic; one of the originalities lies in the distributed implementation of the latter process. The approach also focus on the termination, completeness and optimisation problems, which are difficult to be dealt with by distributed approaches.

Then we describe the implementation of the approach and provide experimental results. Additionally we test the effectiveness of the min-conflicts heuristic.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [Aarts & Van Laarhoven 87]
    E.H.L. Aarts and P.J.M. Van Laarhoven: “Simulated Annealing: Theory and Applications”, D.Reidel Publishing Company, 1987.Google Scholar
  2. [Aarts & Korst 89]
    E.H.L. Aarts and J.Korst: “Simulated Annealing and Boltzmann Machines: A stochastic approach to combinatorial optimization and neural computing”, A Wiley Interscience Publication, 1989.Google Scholar
  3. [Bonomi & lutton 84]
    E.Bonomi and J.L. Lutton: “The N-city Travelling Salesman Problem: Statistical Mechanics and the Metropolis Algorithm”, SIAM Rev, 26, 1984.Google Scholar
  4. [Briot 89]
    J.P.Briot: “ACTALK: a testbed for classifying and designing actors languages in the SMALLTALK environment”, ECOOP, 1989.Google Scholar
  5. [Chabrier & al 91]
    J.Chabrier, J.J.Chabrier et F.Trousset: “Résolution efficace d'un problème de satisfaction de contraintes: le millions de reines”, International Conference of AI, KBS, ES and NL, Avignon, 1991.Google Scholar
  6. [Dechter & Pearl 87]
    R.Dechter and J.Pearl: “The cycle-cutset method for improving search performance in AI applications”, Proc. third IEEE Conference on AI applications, Orlando, 1987.Google Scholar
  7. [Dechter & Pearl 89]
    R.Dechter and J.Pearl: “Tree clustering for Constraint Networks”, Artificial Intelligence, 38, 1989.Google Scholar
  8. [Ferber & Jacopin 90]
    J.Ferber et E.facopin: “The framework of Eco Problem Solving”, Y.Demazeau et J.P. Müller, v. 1, Decentralized Artificial Intelligence, Editions North Holland, 1990.Google Scholar
  9. [Freuder & Wallace 92]
    E.C.Freuder and R.J.Wallace: “Partial constraint satisfaction”, Artificial Intelligence, 58, 1992.Google Scholar
  10. [Ghedira & Verfaillie 91]
    K.Ghedira and G.Verfaillie: “Approche multi-agents pour le problème d'affectation”, International Conference of AI, KBS, ES and NL, Avignon, 1991.Google Scholar
  11. [Ghedira & Verfaillie 92a]
    K.Ghedira and G.Verfaillie: “Approche multi-agents d'un problème de satisfaction de contraintes: optimalité et réactivité”, International Conference of AI, KBS, ES and NL, Avignon, 1992.Google Scholar
  12. [Ghedira & Verfaillie 92b]
    K.Ghedira and G.Verfaillie: “A multi-agent model for the resource allocation problem: a reactive approach”, ECAI 92, Vienna, 1992; also published in: “Scheduling of Production Process”, Edts by Y.Dorn and K.A.Froeschl, University Vienna, 1993.Google Scholar
  13. [Ghedira 92]
    K.Ghedira: “A reactive and distributed approach to the revision problem in the framework of the resource allocation problem”, ETFA, Melbourne, 1992.Google Scholar
  14. [Ghedira 94a]
    K.Ghedira: “Partial Constraint Satisfaction by a Multi-Agent-Simulated Annealing approach”, International Conference of AI, KBS, ES and NL, Paris, 1994.Google Scholar
  15. [Ghedira 94b]
    K.Ghedira: “Dynamic Partial Constraint Satisfaction by a Multi-Agent-Simulated Annealing approach”, workshop CSP-ECAI, Amsterdam, 1994.Google Scholar
  16. [Haralick & Elliot 80]
    R.M.Haralick and G.L.Elliot: “Increasing tree search efficiency for constraint satisfaction problems”, Artificial Intelligence, 14, 1980.Google Scholar
  17. [Jegou 91]
    P.Jégou: “Contribution à l'étude des problèmes de satisfaction de contraintes: algorithmes de propagation et de résolution, propagation de contraintes dans les réseaux dynamiques”, PHD-Thesis, LIRMM-USTL Montpellier II, 1991.Google Scholar
  18. [Johnston & al 92]
    S.Minton, M.D.Johnston, A.B.Philips et P.Laird: “Minimizing conflicts: a heuristic repair method for constraint satisfaction problem and scheduling problems”, Artificial Intelligence, 1992.Google Scholar
  19. [Kirkpatrick & al 93 S.Kirkpatrick, C.D.Gelatt, Jr.M.P.Vecchi:]
    “Optimisation by simulated annealing”, Science, 220, 1983.Google Scholar
  20. [Kuwabara & al 90]
    M.Yokoo, T.Ishida and K.Kuwabara: “Distributed Constraint Satisfaction for DAI Problems”, Proc. 10th International Workshop on Distributed Artificial Intelligence, 1990.Google Scholar
  21. [Kuwabara & al 91]
    Y.Nishibe, K.Kuwabara and T.Ishida: “Effects of Heuristics in Distributed Constraint Satisfaction: Towards Satisficing Algorithms”, Proc. 10th International Workshop on Distributed Artificial Intelligence, 1990.Google Scholar
  22. [Liu & Sycara 93]
    J.Liu & K.Sycara: “Emergent Constraint Satisfaction through Multi-agent Coordinated Interaction”, K.Ghedira & F.Sprumont: proc of MAAMAW'93, Neuchatel, Switzerland, 1993.Google Scholar
  23. [Metropolis & al 53]
    N.Metrolpolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller and E.Teller: “Simulated Annealing”, J. Chem. Pys. 21, 1953.Google Scholar
  24. [Minton & al 92]
    S.Minton, M.D.Johnston, A.B.Philips and P.Laird: “Minimizing conflicts: a heuristic repair method for constraint satisfaction problem and scheduling problems”, Artificial Intelligence, 1992.Google Scholar
  25. [Montanari 74]
    U.Montanari: “Networks of constraints: fundamental properties and applications to picture processing”, Information Sciences, 7, 1974.Google Scholar
  26. [Nadel 89]
    B.A. Nadel: “Constraint satisfaction algorithms”, Search in artificial intelligence, Editions L.Kanal et V.Kumar, Springer Verlag, 1989.Google Scholar
  27. [Selman & Kautz 93]
    B.Selman & H.Kautz: “Domain-Independent Extensions to GSAT: Solving Large Structured Satisfiability Problems”, IJCAI, 1993.Google Scholar
  28. [Verfaillie 93]
    G.Verfaillie: “Problèmes de satisfaction de contraintes: production et révision de solution par modifications locales”, International Conference of AI, KBS, ES and NL, Avignon, 1993.Google Scholar
  29. [Weisbuch 89]
    G.Weisbuch: “Dynamique des systèmes complexes: une introduction aux réseaux d'automates”, Intereditions/Editions CNRS.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Khaled Ghedira
    • 1
  1. 1.Institute of Computer Science and Artificial IntelligenceUniversity of NeuchâtelNeuchâtelSwitzerland

Personalised recommendations