Advertisement

Distributed Splitting of Constraint Satisfaction Problems

  • Farhad Arbab
  • Eric Monfroy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1906)

Abstract

Constraint propagation aims to reduce a constraint satisfaction problem into an equivalent but simpler one. However, constraint propagation must be interleaved with a splitting mechanism in order to compose a complete solver. In [13] a framework for constraint propagation based on a control-driven coordination model was presented. In this paper we extend this framework in order to integrate a distributed splitting mechanism. This technique has three main advantages: 1)in a single distributed and generic framework, propagation and splitting can be interleaved in order to realize complete distributed solvers, 2) by changing only one agent, we can perform different kinds of search, and 3) splitting of variables can be dynamically triggered before the fixed point of a propagation is reached.

Keywords

Constraint Satisfaction Problem Input Port Constraint Propagation Termination Agent Search Agent 
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.
    K. R. Apt. Component-based framework for constraint programming. Manuscript, 1999.Google Scholar
  2. 2.
    K. R. Apt. The Essence of Constraint Propagation. Theoretical Computer Science, 221(1–2):179–210, 1999.MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    K. R. Apt. The Rough Guide to Constraint Propagation”. In J. Jaffar, editor, Proc. of the 5th International Conference on Principles and Practice of Constraint Programming (CP’99), volume 1713 of Lecture Notes in Computer Science, pages 1–23. Springer-Verlag, 1999. Invited lecture.Google Scholar
  4. 4.
    K. R. Apt and E. Monfroy. Automatic Generation of Constraint Propagation Algorithms for Small Finite Domains. In J. Jaffar, editor, Proceedings of Fifth International Conference on Principles and Practice of Constraint Programming, CP’99, volume 1713 of Lecture Notes in Computer Science, pages 58–72, Alexandria, Virginia, USA, October 1999.Google Scholar
  5. 5.
    F. Arbab. Coordination of massively concurrent activities. Technical Report CSR9565, CWI, Amsterdam, The Netherlands, November 1995. Available on-line http://www.cwi.nl/ftp/CWIreports/IS/CS-R9565.ps.Z.Google Scholar
  6. 6.
    F. Arbab. The IWIM model for coordination of concurrent activities. In Paolo Ciancarini and Chris Hankin, editors, Coordination Languages and Models, volume 1061 of Lecture Notes in Computer Science, pages 34–56. Springer-Verlag, 1996.CrossRefGoogle Scholar
  7. 7.
    F. Arbab. Manifold2.0 reference manual. CWI, Amsterdam, The Netherlands, May 1997.Google Scholar
  8. 8.
    F. Benhamou and W. Older. Applying interval arithmetic to real, integer and Boolean constraints. Journal of Logic Programming, 32(1):1–24, March 1997.Google Scholar
  9. 9.
    P. Codognet and D. Diaz. A simple and efficient Boolean constraint solver for constraint logic programming. Journal of Automated Reasoning, 17(1):97–128, 1996.MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    L. Granvilliers. Résolution approchée de contraintes réelles par transformations symboliques et consistance de bloc. Technique et Science Informatiques, 18(2):209–232, 1999.Google Scholar
  11. 11.
    O. Lhomme, A. Gotlieb, and M. Rueher. Dynamic Optimization of Interval Narrowing Algorithms. Journal of Logic Programming, 37(1–2):165–183, 1998.MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    E. Monfroy. Using “Weaker” Functions for Constraint Propagation over Real Numb ers. In J. Carroll, H. Haddad, D. Oppenheim, B. Bryant, and G. Lamont, editors, Proceedings of The 14th ACM Symposium on Applied Computing, ACM SAC’99, Scientific Computing Track, pages 553–559, San Antonio, Texas, USA, March 1999.Google Scholar
  13. 13.
    E. Monfroy. A Coordination-based Chaotic Iteration Algorithm for Constraint Propagation. In J. Carroll, E. Damiani, H. Haddad, and D. Oppenheim, editors, Proceedings of the 2000 ACM Symposium on Applied Computing (SAC’2000), pages 262–269, Villa Olmo, Como, Italy, March 2000. ACM Press.Google Scholar
  14. 14.
    E. Monfroy and F. Arbab. Constraints Solving as the Coordination of Inference Engines, chapter in “Coordination of Internet Agents: Models, Technologies, and Applications”. Springer-Verlag, 2000. To appear.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Farhad Arbab
    • 1
  • Eric Monfroy
    • 1
  1. 1.CWIAmsterdamthe Netherlands

Personalised recommendations