Constraint satisfaction algorithms
Computational Intelligence
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Generalized Arc Consistency with Application to MaxCSP
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Partition-Based Lower Bound for Max-CSP
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A decision theoretic meta-reasoner for constraint optimization
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
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This paper reviews the main approaches for extending arc consistency propagation in constraint optimization frameworks and discusses full and partial arc consistency propagation based on Larrosa's W-NC* and W-AC*2001 algorithms [Larrosa 2002]. We implement these full/partial propagation algorithms in branch and bound search and compare their performance on MaxCSP models. We empirically demonstrate that maintaining arc consistency is more efficient than other partial propagation. We also demonstrate that the end result of constraint propagation can be used as an effective heuristic for guiding search in constraint optimization problems.