Advertisement

Handling Over-Constrained Problems in Distributed Multi-agent Systems

  • Lingzhong Zhou
  • Abdul Sattar
  • Scott Goodwin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3501)

Abstract

The distributed constraint satisfaction problem is a general framework used to represent problems in distributed multi-agent systems. In this paper, we describe a detailed investigation of handling over-constrained satisfaction problems in a dynamic and multi-agent environment. We introduce a new algorithm, Over-constrained Dynamic Agent Ordering, that treats under and over-constrained problems uniformly. While the existing approaches generally only consider a single variable per agent, the proposed algorithm can handle multiple variables per agent. In this approach, we use the degree of unsatisfiability as a measure for relaxing constraints, and hence as a way to guide the search towards the best possible solution(s). Through an experimental study, we demonstrate that our algorithm performs better than the one based on asynchronous weak commitment search.

Keywords

Multiagent System Constraint Satisfaction Problem Constraint Density Neighbouring Agent Constraint Relaxation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Freuder, E.C., Wallace, R.J.: Partial constraint satisfaction. Artificial Intelligence 58(1-3), 21–70 (1992)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Hirayama, K., Yokoo, M.: Distributed partial constraint satisfaction problem. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 222–236. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  3. 3.
    Hirayama, K., Yokoo, M.: An approach to over-constrained distributed constraint satisfaction problems: Distributed hierarchical constraint satisfact. In: Proceedings of the Fourth International Conference on MultiAgent Systems, ICMAS 2000 (2000)Google Scholar
  4. 4.
    Mailler, R., Lesser, V.: Solving Distributed Constraint Optimization Problems Using Cooperative Mediation. In: AAMAS 2004, pp. 438–445. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  5. 5.
    Minton, S., Johnston, M.D., Philips, A.B., Laird, P.: Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence, 161–205 (1992)Google Scholar
  6. 6.
    Modi, P.J., Shen, W.-M., Tambe, M., Yokoo, M.: An asynchronous complete method for distributed constraint optimization. In: Proceedings of the second international joint conference on Autonomous agents and multiagent systems, pp. 161–168. ACM Press, New York (2003)CrossRefGoogle Scholar
  7. 7.
    Yokoo, M.: Constraint relaxation in distributed constraint satisfaction problem. In: Proceedings of 5th International Conference on Tools with Artificial Intelligence, pp. 56–63 (1993)Google Scholar
  8. 8.
    Yokoo, M., Durfee, E.H.: Distributed constraint optimization as a formal model of partially adversarial cooperation. Technical Report CSE-TR-101-91, Ann Arbor, MI 48109 (1991)Google Scholar
  9. 9.
    Yokoo, M., Hirayama, K.: Distributed constraint satisfaction algorithm for complex local problems. In: Proceedings of the Third International Conference on Multiagent Systems (ICMAS 1998), pp. 372–379 (1998)Google Scholar
  10. 10.
    Zhou, L., Thornton, J., Sattar, A.: Dynamic agent ordering in distributed constraint satisfaction problems. In: Gedeon, T(T.) D., Fung, L.C.C. (eds.) AI 2003. LNCS (LNAI), vol. 2903, pp. 427–439. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Lingzhong Zhou
    • 1
  • Abdul Sattar
    • 1
  • Scott Goodwin
    • 2
  1. 1.Institute for Integrated and Intelligent SystemsGriffith UniversityBrisbaneAustralia
  2. 2.School of Computer ScienceUniversity of WindsorCanada

Personalised recommendations