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Uncertainty in constraint satisfaction problems: A probabilistic approach

  • Hélène Fargier
  • Jérôme Lang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 747)

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).

Keywords

Real Problem Constraint Satisfaction Problem White Wine Belief Function Approximate Reasoning 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Hélène Fargier
    • 1
  • Jérôme Lang
    • 1
  1. 1.Institut de Recherche en Informatique de Toulouse (I.R.I.T.) - C.N.R.S.Université Paul SabatierToulouse CedexFrance

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