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Maintaining Arc-Consistency within Dynamic Backtracking

  • Narendra Jussien
  • Romuald Debruyne
  • Patrice Boizumault
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1894)

Abstract

Most of complete search algorithms over Constraint Satisfaction Problems (csp) are based on Standard Backtracking. Two main enhancements of this basic scheme have been studied: first, to integrate constraint propagation as mac which maintains arc consistency during search; second, intelligent backtrackers which avoid repeatedly falling in the same dead-ends by recording nogoods as Conflict-directed Back Jumping (cbj) or Dynamic Backtracking (dbt). Integrations of constraint propagation within intelligent backtrackers have been done as mac-cbj which maintains arc consistency in cbj. However, Bessière and Régin have shown that mac-cbj was very rarely better than mac. However, the inadequacy of mac-cbj is more related to the fact that cbj does not avoid thrashing than to the cost of the management of nogoods.

This paper describes and evaluates mac-dbt which maintains arc-consistency in dbt. Experiments show that mac-dbt is able to solve very large problems and that it remains very stable as the size of the problems increases. Moreover, mac-dbt outperforms mac on the structured problems we have randomly generated.

Keywords

Constraint Satisfaction Problem Constraint Propagation Partial Assignment Current Partial Assignment Open Shop Problem 
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 2000

Authors and Affiliations

  • Narendra Jussien
    • 1
  • Romuald Debruyne
    • 1
  • Patrice Boizumault
    • 1
  1. 1.École des Mines de NantesNantes Cedex 3

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