Skip to main content
SpringerLink
Log in
Book cover

European Workshop on Modelling Autonomous Agents in a Multi-Agent World

MAAMAW 1994: Distributed Software Agents and Applications pp 106–122Cite as

A distributed approach to partial constraint satisfaction problems

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. E.H.L. Aarts and P.J.M. Van Laarhoven: “Simulated Annealing: Theory and Applications”, D.Reidel Publishing Company, 1987.

    Google Scholar 

  2. 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. 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. J.P.Briot: “ACTALK: a testbed for classifying and designing actors languages in the SMALLTALK environment”, ECOOP, 1989.

    Google Scholar 

  5. 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. 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. R.Dechter and J.Pearl: “Tree clustering for Constraint Networks”, Artificial Intelligence, 38, 1989.

    Google Scholar 

  8. 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. E.C.Freuder and R.J.Wallace: “Partial constraint satisfaction”, Artificial Intelligence, 58, 1992.

    Google Scholar 

  10. 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. 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. 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. 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. 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. K.Ghedira: “Dynamic Partial Constraint Satisfaction by a Multi-Agent-Simulated Annealing approach”, workshop CSP-ECAI, Amsterdam, 1994.

    Google Scholar 

  16. R.M.Haralick and G.L.Elliot: “Increasing tree search efficiency for constraint satisfaction problems”, Artificial Intelligence, 14, 1980.

    Google Scholar 

  17. 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. 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. “Optimisation by simulated annealing”, Science, 220, 1983.

    Google Scholar 

  20. 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. 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. 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. N.Metrolpolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller and E.Teller: “Simulated Annealing”, J. Chem. Pys. 21, 1953.

    Google Scholar 

  24. 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. U.Montanari: “Networks of constraints: fundamental properties and applications to picture processing”, Information Sciences, 7, 1974.

    Google Scholar 

  26. B.A. Nadel: “Constraint satisfaction algorithms”, Search in artificial intelligence, Editions L.Kanal et V.Kumar, Springer Verlag, 1989.

    Google Scholar 

  27. B.Selman & H.Kautz: “Domain-Independent Extensions to GSAT: Solving Large Structured Satisfiability Problems”, IJCAI, 1993.

    Google Scholar 

  28. 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. G.Weisbuch: “Dynamique des systèmes complexes: une introduction aux réseaux d'automates”, Intereditions/Editions CNRS.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science and Artificial Intelligence, University of Neuchâtel, Emile Argand, 11, 2007, Neuchâtel, Switzerland

    Khaled Ghedira

Authors
  1. Khaled Ghedira
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

John W. Perram Jean-Pierre Müller

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ghedira, K. (1996). A distributed approach to partial constraint satisfaction problems. In: Perram, J.W., Müller, JP. (eds) Distributed Software Agents and Applications. MAAMAW 1994. Lecture Notes in Computer Science, vol 1069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61157-6_25

Download citation

  • DOI: https://doi.org/10.1007/3-540-61157-6_25

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61157-8

  • Online ISBN: 978-3-540-68335-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation