Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Compiling constraint satisfaction problems
Artificial Intelligence
Radio Link Frequency Assignment
Constraints
Domain transmutation in constraint satisfaction problems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Neighborhood interchangeability and dynamic bundling for non-binary finite CSPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Exact phase transitions in random constraint satisfaction problems
Journal of Artificial Intelligence Research
Exploiting interchangeabilities in constraint satisfaction problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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This paper introduces directional interchangeability, a weak form of neighborhood interchangeability [6]. The basic idea is that although two values of a variable may not be neighborhood interchangeable if we consider the whole neighborhood of the variable, they could be neighborhood interchangeable if we restrict the neighborhood to a subset of neighboring variables induced by a variable ordering.In spite of the fact that the proposed concept cannot be used to remove redundant values while preserving problem satisfiability, it provides a mean to partition value domains into subsets of directionally interchangeable values that can be attempted simultaneously by a tree search.Several experiments carried out on various binary CSPs, clearly indicate that variations of the Forward-Checking algorithm and the Maintaining Arc-Consistency algorithm that exploit directional interchangeability often outperform the original algorithms.