Skip to main content

Towards a more efficient stochastic constraint solver

  • Papers
  • Conference paper
  • First Online:
Principles and Practice of Constraint Programming — CP96 (CP 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1118))

Abstract

E-GENET shows certain success on extending GENET for non-binary CSP's. However, the generic constraint representation scheme of E-GENET induces the problem of storing too many penalty values in constraint nodes and the min-conflicts heuristic is not efficient enough on some problems. To overcome these two weaknesses and further improve the performance, we propose several modifications. All of them together can boost the efficiency of E-GENET without resorting to modifying the underlying network model or the convergence procedure in an ad hoc manner. The performance of modified E-GENET also compares well against that of CHIP.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. N. Beldiceanu and E. Contejean. Introducing global constraints in CHIP. Journal of Mathematical and Computer Modelling, 17(7):57–73, 1994.

    Google Scholar 

  2. A. Davenport, E. Tsang, C. J. Wang, and K. Zhu. GENET: A connectionist architecture for solving constraint satisfaction problems by iterative improvement. In Proc. 12th National Conference on Artificial Intelligence, 1994.

    Google Scholar 

  3. D. Diaz and P. Codognet. A minimal extension of the WAM for clp(FD). In Proc. 10th International Conference on Logic Programming, pages 774–790, 1993.

    Google Scholar 

  4. M. Dincbas, H. Simonis, and P. Van Hentenryck. Solving car sequencing problem in contraint logic programming. In Proc. European Conference on AI, pages 290–295, 1988.

    Google Scholar 

  5. M. Dincbas, P. Van Hentenryck, H. Simonis, A. Aggoun, T. Graf, and F. Berthier. The constraint logic programming language CHIP. In Proc. International Conference on Fifth Generation Computer Systems, pages 693–702, December 1988.

    Google Scholar 

  6. E. C. Freuder. Partial constraint satisfaction. In Proc. 11th International Joint Conference on AI, pages 278–283, 1989.

    Google Scholar 

  7. J.H.M. Lee, H.F. Leung, and H.W. Won. Extending GENET for non-binary CSP's. In Proc. 7th International Conference on Tools with Artificial Intelligence, pages 338–343, 1995.

    Google Scholar 

  8. S. Minton, M. D. Johnston, A. B. Philips, and P. Laird. Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence, 58:161–205, 1992.

    Google Scholar 

  9. The COSYTEC Team. CHIP V4.1 User Manuals, 1994.

    Google Scholar 

  10. P. Van Hentenryck. Constraint Satisfaction in Logic Programming. The MIT Press, 1989.

    Google Scholar 

  11. T. Warwick and E.P.K. Tsang. Tackling car sequencing problems using a generic genetic algorithm. Evolutionary Computation, 3(3):267–298, 1995. (to appear).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Eugene C. Freuder

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lee, J.H.M., Leung, Hf., Won, Hw. (1996). Towards a more efficient stochastic constraint solver. In: Freuder, E.C. (eds) Principles and Practice of Constraint Programming — CP96. CP 1996. Lecture Notes in Computer Science, vol 1118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61551-2_85

Download citation

  • DOI: https://doi.org/10.1007/3-540-61551-2_85

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61551-4

  • Online ISBN: 978-3-540-70620-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics