Using inferred disjunctive constraints to decompose constraint satisfaction problems

Authors:
Eugene C. Freuder;Paul D. Hubbe
Affiliations:
Department of Computer Science, University of New Hampshire, Durham, New Hampshire;Department of Computer Science, University of New Hampshire, Durham, New Hampshire
Venue:
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Year:
1993

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Abstract

Constraint satisfaction problems involve finding values for problem variables that satisfy constraints on what combinations of values are permitted. They have applications in many areas of artificial intelligence, from planning to natural language understanding. A new method is proposed for decomposing constraint satisfaction problems using inferred disjunctive constraints. The decomposition reduces the size of the problem. Some solutions may be lost in the process, but not all. The decomposition supports an algorithm that exhibits superior performance. Analytical and experimental evidence suggests that the algorithm can take advantage of local weak spots in globally hard problems.