A Framework for Decision-Based Consistencies

  • Jean-François Condotta
  • Christophe Lecoutre
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6876)


Consistencies are properties of constraint networks that can be enforced by appropriate algorithms to reduce the size of the search space to be explored. Recently, many consistencies built upon taking decisions (most often, variable assignments) and stronger than (generalized) arc consistency have been introduced. In this paper, our ambition is to present a clear picture of decision-based consistencies. We identify four general classes (or levels) of decision-based consistencies, denoted by \(S^{\phi}_{\Delta}\) Open image in new window , Open image in new window and Open image in new window , study their relationships, and show that known consistencies are particular cases of these classes. Interestingly, this general framework provides us with a better insight into decision-based consistencies, and allows us to derive many new consistencies that can be directly integrated and compared with other ones.


Decision Mapping Unary Constraint Strong Consistency Constraint Network Negative Decision 
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 2011

Authors and Affiliations

  • Jean-François Condotta
    • 1
  • Christophe Lecoutre
    • 1
  1. 1.CRIL - CNRS, UMR 8188Univ Lille Nord de France, ArtoisLensFrance

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