Domain filtering consistencies for non-binary constraints
Artificial Intelligence
Optimization of Simple Tabular Reduction for Table Constraints
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
A fast arc consistency algorithm for n-ary constraints
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Data structures for generalised arc consistency for extensional constraints
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Domain filtering consistencies
Journal of Artificial Intelligence Research
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
An optimal coarse-grained arc consistency algorithm
Artificial Intelligence
Generalized arc consistency for positive table constraints
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
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Generalized arc consistency (GAC) is the most widely used local consistency in constraint programming. Several GAC algorithms for specific constraints, as well as generic algorithms that can be used on any constraint, have been proposed in the literature. Stronger local consistencies than GAC have also been studied but algorithms for such consistencies are generally considered too expensive. In this paper we propose an extension to the standard GAC algorithm GAC2001/3.1 that achieves a stronger local consistency than GAC by considering intersections of constraints. Importantly, the worst-case time complexity of the proposed algorithm, called GAC+, is higher than that of GAC2001/3.1 only by a factor e, where e is the number of constraints in the problem. Experimental results demonstrate that in many cases GAC+ can reduce the size of the search tree compared to GAC, resulting in improved cpu times. Also, in cases where there is no gain in search tree size, there is only a negligible overhead in cpu time.