Arc and path consistence revisited
Artificial Intelligence
Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
From local to global consistency
Artificial Intelligence
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
Fast parallel constraint satisfaction
Artificial Intelligence
Characterising tractable constraints
Artificial Intelligence
On the minimality and global consistency of row-convex constraint networks
Journal of the ACM (JACM)
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
Synthesizing constraint expressions
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Constraint Representation for Propagation
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Increasing functional constraints need to be checked only once
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Domain filtering consistencies for non-binary constraints
Artificial Intelligence
Inverse Consistencies for Non-binary Constraints
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
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Local consistency enforcing is at the core of CSP (Constraint Satisfaction Problem) solving. Although arc consistency is still the most widely used level of local consistency, researchers are going on investigating more powerful levels, such as path consistency, k-consistency, (i,j)-consistency. Recently, more attention has been turned to inverse local consistency levels, such as path inverse consistency, k-inverse consistency, neighborhood inverse consistency, which do not suffer from the drawbacks of the other local consistency levels (changes in the constraint definitions and in the constraint graph, prohibitive memory requirements).In this paper, we propose a generic framework for inverse local consistency, which includes most of the previously defined levels and allows a rich set of new levels to be defined. The first benefit of such a generic framework is to allow a user to define and test many different inverse local consistency levels, in accordance with the problem or even the instance he/she has to solve. The second benefit is to allow a generic algorithm to be defined. This algorithm, which is parameterized by the chosen inverse local consistency level, generalizes the AC7 algorithm used for arc consistency, and produces from any instance its locally consistent closure at the chosen level.