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On the Behavior and Application of Constraint Weighting

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Principles and Practice of Constraint Programming – CP’99 (CP 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1713))

Abstract

In this paper we compare the performance of three constraint weighting schemes with one of the latest and fastest WSAT heuristics: rnovelty. We extend previous results from satisfiability testing by looking at the broader domain of constraint satisfaction and test for differences in performance using randomly generated problems and problems based on realistic situations and assumptions. We find constraint weighting produces fairly consistent behaviour within problem domains, and is more influenced by the number and interconnectedness of constraints than the realism or randomness of a problem. We conclude that constraint weighting is better suited to smaller structured problems, where it is can clearly distinguish between different constraint groups.

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References

  1. Cha, B., Iwama, K.: Performance test of local search algorithms using new types of random CNF formulas. In: Proceedings of IJCAI 1995, pp. 304–310 (1995)

    Google Scholar 

  2. Davenport, A., Tsang, E., Wang, C., Zhu, K.: GENET: A connectionist architecture for solving constraint satisfaction problems by iterative improvement. In: Proceedings of AAAI 1994, pp. 325–330 (1994)

    Google Scholar 

  3. Frank, J.: Weighting for Godot: Learning heuristics for GSAT. In: Proceedings of AAAI 1996, pp. 338–343 (1996)

    Google Scholar 

  4. Frank, J.: Learning short-term weights for GSAT. In: Proceedings of IJCAI 1997, pp. 384–389 (1997)

    Google Scholar 

  5. Gent, I., Walsh, T.: Towards an Understanding of Hill-climbing Procedures for SAT. In: Proceedings of AAAI 1993, pp. 28–33 (1993)

    Google Scholar 

  6. Kumar, V.: Algorithms for constraint satisfaction problems: a survey. AI Magazine, 32–43 (Spring 1992)

    Google Scholar 

  7. Mackworth, A.: Consistency in networks of relations. Artificial Intelligence 8(1), 99–118 (1977)

    Article  MATH  MathSciNet  Google Scholar 

  8. McAllester, D., Selman, B., Kautz, H.: Evidence for invariants in local search. In: Proceedings of AAAI 1997, pp. 321–326 (1997)

    Google Scholar 

  9. Morris, P.: The Breakout method for escaping from local minima. In: Proceedings of AAAI 1993, pp. 40–45 (1993)

    Google Scholar 

  10. Prosser, P.: An empirical study of phase transitions in binary constraint satisfaction problems. Artificial Intelligence 81(1-2), 81–111 (1996)

    Article  MathSciNet  Google Scholar 

  11. Selman, B., Levesque, H., McAllester, D.: A new method for solving hard satisfiability problems. In: Proceedings of AAAI 1992, pp. 440–446 (1992)

    Google Scholar 

  12. Selman, B., Kautz, H.: Domain-independent extensions to GSAT: solving large structured satisfiability problems. In: Proceedings of IJCAI 1993, pp. 290–295 (1993)

    Google Scholar 

  13. Selman, B., Kautz, H., McAllester, D.: Ten challenges in propositional reasoning and search. In: Proceedings of IJCAI 1997, pp. 50–54 (1997)

    Google Scholar 

  14. Thornton, J., Sattar, A.: Dynamic Constraint Weighting for Over-Constrained Problems. In: Proceedings of PRICAI 1998, pp. 377–388 (1998)

    Google Scholar 

  15. Voudouris, C., Tsang, E.: Partial Constraint Satisfaction Problems and Guided Local Search. In: Proceedings of Practical Application of Constraint Technology (PACT 1996), pp. 337–356 (1996)

    Google Scholar 

  16. Walser, J., Iyer, R., Venkatasubramanyan, N.: An integer local search method with application to capacitated production planning. In: Proceedings of AAAI 1998, pp. 373–379 (1998)

    Google Scholar 

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© 1999 Springer-Verlag Berlin Heidelberg

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Thornton, J., Sattar, A. (1999). On the Behavior and Application of Constraint Weighting. In: Jaffar, J. (eds) Principles and Practice of Constraint Programming – CP’99. CP 1999. Lecture Notes in Computer Science, vol 1713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48085-3_32

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  • DOI: https://doi.org/10.1007/978-3-540-48085-3_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66626-4

  • Online ISBN: 978-3-540-48085-3

  • eBook Packages: Springer Book Archive

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