Maintaining Arc-Consistency within Dynamic Backtracking
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.
KeywordsConstraint Satisfaction Problem Constraint Propagation Partial Assignment Current Partial Assignment Open Shop Problem
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