Contradicting conventional wisdom in constraint satisfaction

  • Daniel Sabin
  • Eugene C. Freuder
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 874)


Constraint satisfaction problems have wide application in artificial intelligence. They involve finding values for problem variables where the values must be consistent in that they satisfy restrictions on which combinations of values are allowed. Two standard techniques used in solving such problems are backtrack search and consistency inference. Conventional wisdom in the constraint satisfaction community suggests: 1) using consistency inference as preprocessing before search to prune values from consideration reduces subsequent search effort and 2) using consistency inference during search to prune values from consideration is best done at the limited level embodied in the forward checking algorithm. We present evidence contradicting both pieces of conventional wisdom, and suggesting renewed consideration of an approach which fully maintains arc consistency during backtrack search.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Daniel Sabin
    • 1
  • Eugene C. Freuder
    • 1
  1. 1.Department of Computer ScienceUniversity of New HampshireDurhamUSA

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