Sparse constraint graphs and exceptionally hard problems

  • Authors:

  • Barbara M. Smith;Stuart A. Grant

  • Affiliations:

  • Division of Artificial Intelligence, School of Computer Studies, University of Leeds, Leeds, UK;Division of Artificial Intelligence, School of Computer Studies, University of Leeds, Leeds, UK

  • Venue:
  • IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
  • Year:
  • 1995

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Abstract

Many types of problem exhibit a phase transition as a problem parameter is varied, from a region where most problems are easy and soluble to a region where most problems are easy but insoluble. In the intervening phase transition region, the median problem difficulty is greatest. However, occasional exceptionally hard problems (ehps) can be found in the easy and soluble region; these problems can be much harder than any problem occurring in the phase transition. We show that, in binary constraint satisfaction problems, ehps are much more likely to occur when the constraints are sparse than when they are dense. Ehps occur when the search algorithm encounters a large insoluble subproblem at an early stage; the exceptional difficulty is due to the cost of searching the subproblem to prove insolubility. This cost can be dramatically reduced by using conflict-directed backjumping (CBJ) rather than a chronological backtracker. However, when used with forward checking and the fail-first heuristic, it is only on ehps that CBJ gives great savings over backtracking chronologically.