Abstract
We propose a framework for dealing with probabilistic uncertainty in constraint satisfaction problems, associating with each constraint the probability that it is a part of the real problem (the latter being only partially known). The probability degrees on the relevance of the constraints enable us to define, for each instanciation, the probability that it is a solution of the real problem. We briefly give a methodology for the search of the best solution (maximizing this probability).
Preview
Unable to display preview. Download preview PDF.
References
Bel G., Bensana E., Berlandier P., David P., Fargier H., Gaspin C., Ghedira K., Janssen P., Jégou P., Kökeny T., Lang J., Lesaint D., Martin-Clouaire R., Neveu B., Rellier J.P., Schiex T., Trousse B., Verfaillie G., Vilarem M.C. (1992), Représentation et traitement pratique de la flexibilité dans les problèmes sous contraintes (in French), Actes des Journées du PRC-IA, Teknea, 369–428.
Chatalic P., Dubois D., Prade H. (1987) An Approach to Approximate Reasoning Based on the Dempster Rule of Combination. International Journal of Expert Systems 1 (1), 647–85.
Clarke M., Gabbay D. M. (1993) Probability of provability, manuscript.
Dechter A., Dechter R. (1988) Belief Maintenance in Dynamic Constraints Networks. Proc. AAAI 88, 37–42.
Dechter R., Pearl J. (1989) Tree clustering for constraint networks. Artificial Intelligence 38, 353–366.
Dubois D., Fargier H., Prade H. (1993), The calculus of fuzzy restrictions as a basis for flexible constraint satisfaction, Proc. 2nd IEEE Conf. on Fuzzy Sets, 1131–1136.
Freuder E.C., Wallace R.J. (1992), Partial Constraint Satisfaction, Artificial Intelligence, 58(1–3), 21–70.
Haralick R. M., Elliott G. L. (1980) Increasing Tree Search Efficiency for Constraint Satisfation Problems, Artificial Intelligence 14, 263–313.
Mackworth A. K. (1977) Consistency in networks of relations. Artificial Intelligence 8, 99–118.
Martin-Clouaire R., Rellier J. P. (1993) personal communication. To appear in IJCAI Workshop on AI in Agriculture, Natural Resources and Environmental Sciences.
Montanari H. (1974) Networks of Constraints: Fundamental Properties and Application to Picture Processing. Information Science 7, 1974, 95–132.
Pearl J. (1988), Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufman.
Shafer G., Shenoy P (1988) Local computation in hypertrees, Working paper N.201. School of Business, University of Kansas, 1988.
Schiex T. (1992) Possibilistic constraint satisfaction problems or how to handle soft constraints. Proc. 8th Conf. on Uncertainty in AI, 268–275.
Smets P. (1988), Belief Functions, in Non-Standard Logics for Automated Reasoning (P. Smets, A. Mamdani, D. Dubois, H. Prade eds.), Academic Press, 253–286.
Smets P. (1993) personal communication.
Van Hentenryck P. (1990) Incremental constraint satisfaction in logic programming. ICLP 90, 189–202.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fargier, H., Lang, J. (1993). Uncertainty in constraint satisfaction problems: A probabilistic approach. In: Clarke, M., Kruse, R., Moral, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1993. Lecture Notes in Computer Science, vol 747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028188
Download citation
DOI: https://doi.org/10.1007/BFb0028188
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-57395-1
Online ISBN: 978-3-540-48130-0
eBook Packages: Springer Book Archive