Distributed Constraint Programming with Agents

  • Carl Christian Rolf
  • Krzysztof Kuchcinski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6943)

Abstract

Many combinatorial optimization problems lend themselves to be modeled as distributed constraint optimization problems (DisCOP). Problems such as job shop scheduling have an intuitive matching between agents and machines. In distributed constraint problems, agents control variables and are connected via constraints. We have equipped these agents with a full constraint solver. This makes it possible to use global constraint and advanced search schemes.

By empowering the agents with their own solver, we overcome the low performance that often haunts distributed constraint satisfaction problems (DisCSP). By using global constraints, we achieve far greater pruning than traditional DisCSP models. Hence, we dramatically reduce communication between agents.

Our experiments show that both global constraints and advanced search schemes are necessary to optimize job shop schedules using DisCSP.

Keywords

Constraint Programming Precedence Constraint Constraint Satisfaction Problem Global Constraint Advanced Search 
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.
    Baptiste, P., Pape, C.L., Nuijten, W.: Constraint-Based Scheduling. Kluwer Academic Publishers, USA (2001)CrossRefMATHGoogle Scholar
  2. 2.
    Caseau, Y., Laburthe, F.: Improving branch and bound for jobshop scheduling with constraint propagation. In: Combinatorics and Computer Science, pp. 129–149 (1995)Google Scholar
  3. 3.
    Chandy, K.M., Lamport, L.: Distributed snapshots: determining global states of distributed systems. ACM Trans. Comput. Syst. 3, 63–75 (1985)CrossRefGoogle Scholar
  4. 4.
    Ezzahir, R., Bessiere, C., Belaissaoui, M., Bouyakhf, E.: DisChoco: A platform for distributed constraint programming. In: Proceedings of the IJCAI 2007 Distributed Constraint Reasoning Workshop (DCR 2007), pp. 16–27 (2007)Google Scholar
  5. 5.
    Gallager, R.G., Humblet, P.A., Spira, P.M.: A distributed algorithm for minimum-weight spanning trees. ACM Trans. Program. Lang. Syst. 5, 66–77 (1983)CrossRefMATHGoogle Scholar
  6. 6.
    Gershman, A., Meisels, A., Zivan, R.: Asynchronous forward bounding for distributed COPs. Journal of Artificial Intelligence Research 34, 61–88 (2010)MathSciNetMATHGoogle Scholar
  7. 7.
    Hoare, C.A.R.: Communicating sequential processes. Commun. ACM 21, 666–677 (1978)CrossRefMATHGoogle Scholar
  8. 8.
    Kuchcinski, K.: Constraints-driven scheduling and resource assignment. ACM Transactions on Design Automation of Electronic Systems (TODAES) 8(3), 355–383 (2003)CrossRefGoogle Scholar
  9. 9.
    Kvarnstrom, J., Doherty, P.: Automated planning for collaborative UAV systems. In: 11th International Conference on Control Automation Robotics Vision, pp. 1078–1085 (2010)Google Scholar
  10. 10.
    Lawrence, S.R.: Resource-constrained project scheduling: An experimental investigation of heuristic scheduling techniques. Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh PA (1984)Google Scholar
  11. 11.
    Léauté, T., Ottens, B., Szymanek, R.: FRODO 2.0: An open-source framework for distributed constraint optimization. In: Proceedings of the IJCAI 2009 Distributed Constraint Reasoning Workshop (DCR 2009), pp. 160–164 (2009)Google Scholar
  12. 12.
    Meisels, A., Kaplansky, E.: Scheduling agents – distributed timetabling problems(DisTTP). In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 166–177. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Meisels, A., Zivan, R.: Asynchronous forward-checking for DisCSPs. Constraints 12, 131–150 (2007)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Odersky, M., Spoon, L., Venners, B.: Programming in Scala: A Comprehensive Step-by-step Guide, 1st edn. Artima Incorporation, USA (2008)Google Scholar
  15. 15.
    Rao, V. N., Kumar, V.: Superlinear speedup in parallel state-space search. In: Kumar, S., Nori, K.V. (eds.) FSTTCS 1988. LNCS, vol. 338, pp. 161–174. Springer, Heidelberg (1988)CrossRefGoogle Scholar
  16. 16.
    Rolf, C.C., Kuchcinski, K.: Load-balancing methods for parallel and distributed constraint solving. In: The 10th IEEE International Conference on Cluster Computing, pp. 304–309 (2008)Google Scholar
  17. 17.
    Rossi, F., van Beek, P., Walsh, T.: Handbook of Constraint Programming (Foundations of Artificial Intelligence). Elsevier Science Inc., New York (2006)MATHGoogle Scholar
  18. 18.
    Salido, M.: Distributed cSPs: Why it is assumed a variable per agent? In: Miguel, I., Ruml, W. (eds.) SARA 2007. LNCS (LNAI), vol. 4612, pp. 407–408. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Thompson, G.L.: Industrial scheduling. In: Muth, J.F., Thompson, G.L. (eds.) Collaboration of P.R. Winters. Prentice-Hall, Englewood Cliffs (1963)Google Scholar
  20. 20.
    Yokoo, M., Hirayama, K.: Algorithms for distributed constraint satisfaction: A review. Autonomous Agents and Multi-Agent Systems 3(2), 185–207 (2000)CrossRefGoogle Scholar
  21. 21.
    Yokoo, M., Suzuki, K., Hirayama, K.: Secure distributed constraint satisfaction: Reaching agreement without revealing private information. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 43–66. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carl Christian Rolf
    • 1
  • Krzysztof Kuchcinski
    • 1
  1. 1.Department of Computer ScienceLund UniversitySweden

Personalised recommendations