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
Probabilistic consistency boosts MAC and SAC
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Bit-vector algorithms for binary constraint satisfaction and subgraph isomorphism
Journal of Experimental Algorithmics (JEA)
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Many backtrack search algorithms has been designed over the last years to solve constraint satisfaction problems. Among them, Forward Checking (FC) and Maintaining Arc Consistency (MAC) algorithms are the most popular and studied algorithms. In this paper, such algorithms are revisited and extensively compared giving rise to interesting characterization of their efficiency with respect to random instances. More precisely, we provide experimental evidence that FC outperforms MAC on hard CSPs with high graph density and low constraint tightness whereas MAC is better on hard CSPs with low density and high constraints tightness. This results show that on some CSPs maintaining full arc consistency during search might be time consuming. Then, we propose a new generic approach that maintain partial and parameterizable form of local consistency.