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IJCAI 2007

Conference Paper Constraint Satisfaction Artificial Intelligence

Abstract

Hypertree decomposition has been shown to be the most general CSP decomposition method. However, so far the exact methods are not able to find optimal hypertree decompositions of realistic instances. We present a backtracking procedure which, along with isomorphic component detection, results in optimal hypertree decompositions. We also make the procedure generic; variations of which results in two new tractable decompositions: hyperspread and connected hypertree. We show that the hyperspread width is bounded by both the hypertree width and the spread cut width, which solves a recently stated open problem. In our experiments on several realistic instances, our methods find many optimal decompositions, while the previous methods can find at most one.

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Context

Venue
International Joint Conference on Artificial Intelligence
Archive span
1969-2025
Indexed papers
14525
Paper id
961024921763390740
v2026.09.13