Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
A Comparison of Structural CSP Decomposition Methods
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Consistency Maintenance for ABT
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Hybrid backtracking bounded by tree-decomposition of constraint networks
Artificial Intelligence
Decomposition of distributed nonmonotonic multi-context systems
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
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The algorithm presented here, BCC, is an enhancement of the well known Backtrack used to solve constraint satisfaction problems. Though most backtrack improvements rely on propagation of local informations, BCC uses global knowledge of the constraint graph structure (and in particular its biconnected components) to reduce search space, permanently removing values and compiling partial solutions during exploration. This algorithm performs well by itself, without any filtering, when the biconnected components are small, achieving optimal time complexity in case of a tree. Otherwise, it remains compatible with most existing techniques, adding only a negligible overhead cost.