Probabilistic Arc Consistency: A Connection between Constraint Reasoning and Probabilistic Reasoning
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Constraint Satisfaction Problems: Backtrack Search Revisited
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Refining the basic constraint propagation algorithm
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Optimal and suboptimal singleton arc consistency algorithms
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A greedy approach to establish singleton arc consistency
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Reducing checks and revisions in coarse-grained MAC algorithms
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning How to Propagate Using Random Probing
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Heuristics for Dynamically Adapting Propagation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Improving GASAT by replacing tabu search by DLM and enhancing the best members
Artificial Intelligence Review
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
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Constraint Satisfaction Problems (CSPs) are ubiquitous in Artificial Intelligence. The backtrack algorithms that maintain some local consistency during search have become the de facto standard to solve CSPs. Maintaining higher levels of consistency, generally, reduces the search effort. However, due to ineffective constraint propagation, it often penalises the search algorithm in terms of time. If we can reduce ineffective constraint propagation, then the effectiveness of a search algorithm can be enhanced significantly. In order to do so, we use a probabilistic approach to resolve when to propagate and when not to. The idea is to perform only the useful consistency checking by not seeking a support when there is a high probability that a support exists. The idea of probabilistic support inference is general and can be applied to any kind of local consistency algorithm. However, we shall study its impact with respect to arc consistency and singleton arc consistency (SAC). Experimental results demonstrate that enforcing probabilistic SAC almost always enforces SAC, but it requires significantly less time than SAC. Likewise, maintaining probabilistic arc consistency and maintaining probabilistic SAC require significantly less time than maintaining arc consistency and maintaining SAC.