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Semiring-based CSPs and valued CSPs: Basic properties and comparison

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Over-Constrained Systems (OCS 1995)

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Abstract

In this paper we describe two frameworks for constraint solving where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. One is based on a semiring, and the other one on a totally ordered commutative monoid. We then compare the two approaches and we discuss the relationship between them. The two frameworks have been independently introduced in [2] and [28].

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References

  1. S. Bistarelli. Programmazione con vincoli pesati e ottimizzazione (in italian). Dipartimento di Informatica, Università di Pisa, Italy, 1994.

    Google Scholar 

  2. S. Bistarelli, U. Montanari, and F. Rossi. Constraint Solving over Semirings. In Proc. IJCAI95. Morgan Kaufman, 1995.

    Google Scholar 

  3. A. Borning, M. Maher, A. Martindale, and M. Wilson. Constraint hierarchies and logic programming. In Martelli M. Levi G., editor, Proc. 6th ICLP. MIT Press, 1989.

    Google Scholar 

  4. R. Dechter, A. Dechter, and J. Pearl. Optimization in constraint networks. In R.M Oliver and J.Q. Smith, editors, Influence Diagrams, Belief Nets and Decision Analysis, chapter 18, pages 411–425. John Wiley & Sons Ltd., 1990.

    Google Scholar 

  5. D. Dubois and H. Prade. A class of fuzzy measures based on triangular norms. a general framework for the combination of uncertain information. Int. Journal of Intelligent Systems, 8(1):43–61, 1982.

    Google Scholar 

  6. D. Dubois, H. Fargier, and H. Prade. The calculus of fuzzy restrictions as a basis for flexible constraint satisfaction. In Proc. IEEE Int. Conf. on Fuzzy Systems. IEEE, 1993.

    Google Scholar 

  7. H. Fargier and J. Lang. Uncertainty in constraint satisfaction problems: a probabilistic approach. Proc. ECSQARU. Springer-Verlag, LNCS 747, 1993.

    Google Scholar 

  8. H. Fargier and J. Lang and T. Schiex. Selecting Preferred Solutions in Fuzzy Constraint Satisfaction Problems. Proc. 1st European Congress on Fuzzy and Intelligent Technologies (EUFIT). 1993.

    Google Scholar 

  9. E. Freuder. Synthesizing constraint expressions. CACM, 21(11), 1978.

    Google Scholar 

  10. E. Freuder. Backtrack-free and backtrack-bounded search. In Kanal and Kumar, editors, Search in Artificial Intelligence. Springer-Verlag, 1988.

    Google Scholar 

  11. E. Freuder and R. Wallace. Partial constraint satisfaction. AI Journal, 58, 1992.

    Google Scholar 

  12. M. Garey, D. Johnson, and L. Stockmeyer. Some simplified NP-complete graph problems. Theoretical Computer Science, 1:237–267, 1976.

    Article  Google Scholar 

  13. P. Hubbe and E. Freuder. An efficient cross-product representation of the constraint satisfaction problem search space. In Proc. of AAAI-92, pages 421–427, San Jose, CA, 1992.

    Google Scholar 

  14. V. Kumar. Algorithms for constraint satisfaction problems: a survey. AI Magazine, 13(1), 1992.

    Google Scholar 

  15. J. Jaffar and J.L. Lassez. Constraint Logic Programming. Proc. POPL, ACM, 1987.

    Google Scholar 

  16. M. Krentel. The complexity of optimization problems. Journal of Computer and System Sciences, 36:490–509, 1988.

    Google Scholar 

  17. A. Mackworth. Consistency in networks of relations. AI Journal, 8(1), 1977.

    Google Scholar 

  18. A. Mackworth. Encyclopedia of AI, chapter Constraint Satisfaction, pages 205–211. Springer Verlag, 1988.

    Google Scholar 

  19. A. Mackworth and E. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. AI Journal, 25, 1985.

    Google Scholar 

  20. U. Montanari. Networks of constraints: Fundamental properties and application to picture processing. Information Science, 7, 1974.

    Google Scholar 

  21. U. Montanari and F. Rossi. Constraint relaxation may be perfect. AI Journal, 48:143–170, 1991.

    Google Scholar 

  22. H. Moulin. Axioms for Cooperative Decision Making. Cambridge University Press, 1988.

    Google Scholar 

  23. B. A. Nadel. Constraint satisfaction algorithms. Comput. Intell., 5(4):188–224, November 1989.

    Google Scholar 

  24. C. M. Papadimitriou. Computational Complexity. Addison-Wesley Publishing Company. 1994.

    Google Scholar 

  25. A. Rosenfeld, R. Hummel, S. Zucker. Scene Labelling by Relaxation Operations. IEEE Trans. on Sys., Man, and Cyb., vol. 6. n.6, 1976.

    Google Scholar 

  26. Z. Ruttkay. Fuzzy constraint satisfaction. Proc. 3rd Int. Conf. on Fuzzy Systems, 1994.

    Google Scholar 

  27. T. Schiex. Possibilistic constraint satisfaction problems, or “How to handle soft constraints?”. Proc. 8th Conf. of Uncertainty in AI, 1992.

    Google Scholar 

  28. T. Schiex, H. Fargier, and G. Verfaillie. Valued Constraint Satisfaction Problems: Hard and Easy Problems. In Proc. IJCAI95. Morgan Kaufmann, 1995.

    Google Scholar 

  29. G. Shafer. An axiomatic study of computation in hypertrees. Working paper 232, University of Kansas, School of Business, Lawrence, 1991.

    Google Scholar 

  30. L. Shapiro and R. Haralick. Structural descriptions and inexact matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3:504–519, 1981.

    Google Scholar 

  31. P. Snow and E. Freuder. Improved relaxation and search methods for approximate constraint satisfaction with a maximin criterion. In Proc. of the 8th biennal conf. of the Canadian society for comput. studies of intelligence, pages 227–230, May 1990.

    Google Scholar 

  32. L. Zadeh. Calculus of fuzzy restrictions. In K. Tanaka L.A. Zadeh, K.S. Fu and M. Shimura, editors, Fuzzy sets and their applications to cognitive and decision processes. Academic Press, 1975.

    Google Scholar 

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Michael Jampel Eugene Freuder Michael Maher

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© 1996 Springer-Verlag Berlin Heidelberg

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Bistarelli, S., Faxgier, H., Montanari, U., Rossi, F., Schiex, T., Verfaillie, G. (1996). Semiring-based CSPs and valued CSPs: Basic properties and comparison. In: Jampel, M., Freuder, E., Maher, M. (eds) Over-Constrained Systems. OCS 1995. Lecture Notes in Computer Science, vol 1106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61479-6_19

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  • DOI: https://doi.org/10.1007/3-540-61479-6_19

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