Probabilistic Nogood Store as a Heuristic
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Value ordering for quantified CSPs
Constraints
Heuristics for Dynamically Adapting Propagation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Evaluating and Improving Modern Variable and Revision Ordering Strategies in CSPs
Fundamenta Informaticae - RCRA 2008 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
Learning cluster-based structure to solve constraint satisfaction problems
Annals of Mathematics and Artificial Intelligence
Incorporating variance in impact-based search
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Conflict directed variable selection strategies for constraint satisfaction problems
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
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Identifying structures in a given combinatorial problem is often a key step for designing efficient search heuristics or for understanding the inherent complexity of the problem. Several Operations Research approaches apply decomposition or relaxation strategies upon such a structure identified within a given problem. The next step is to design algorithms that adaptively integrate that kind of information during search. We claim in this paper, inspired by previous work on impact-based search strategies for constraint programming, that using an explanation-based constraint solver may lead to collect invaluable information on the intimate dynamically revealed and static structures of a problem instance. Moreover, we discuss how dedicated OR solving strategies (such as Benders decomposition) could be adapted to constraint programming when specific relationships between variables are exhibited.