Backjump-based backtracking for constraint satisfaction problems

  • Authors:

  • Rina Dechter;Daniel Frost

  • Affiliations:

  • Univ. of California, Irvine;Univ. of California, Irvine

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2002

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Abstract

The performance of backtracking algorithms for solving finite-domain constraint satisfaction problems can be improved substantially by look-back and look-ahead methods. Look-back techniques extract information by analyzing failing search paths that are terminated by dead-ends. Look-ahead techniques use constraint propagation algorithms to avoid such dead-ends altogether. This paper describes a number of look-back variants including backjumping and constraint recording which recognize and avoid some unnecessary explorations of the search space. The last portion of the paper gives an overview of look-ahead methods such as forward checking and dynamic variable ordering, and discusses their combination with backjumping.