On the minimality and global consistency of row-convex constraint networks
Journal of the ACM (JACM)
Constraint tightness and looseness versus local and global consistency
Journal of the ACM (JACM)
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
Tractable tree convex constraint networks
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Set intersection and consistency in constraint networks
Journal of Artificial Intelligence Research
Fast algorithm for connected row convex constraints
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A REVIEW OF TREE CONVEX SETS TEST
Computational Intelligence
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We propose a new framework to study properties of consistency in a Constraint Network from the perspective of properties of set intersection. Our framework comes with a proof schema which gives a generic way of lifting a set intersection property to one on consistency. Various well known results can be derived with this framework. More importantly, we use the framework to obtain a number of new results. We identify a new class of tree convex constraints where local consistency ensures global consistency. Another result is that in a network of arbitrary constraints, local consistency implies global consistency whenever there arc certain m-tight constraints. The most interesting result is that when the constraint on every pair of variables is properly m-tight in an arbitrary network, global consistency can be achieved by enforcing relational m=1-consistency. These results significantly improve our understanding of convex and tight constraints. This demonstrates that our framework is a promising and powerful tool for studying consistency.