New methods to color the vertices of a graph
Communications of the ACM
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Contradicting Conventional Wisdom in Constraint Satisfaction
PPCP '94 Proceedings of the Second International Workshop on Principles and Practice of Constraint Programming
Global Cut Framework for Removing Symmetries
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Constraint Processing
Backjump-Based Techniques versus Conflict-Directed Heuristics
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
An optimal coarse-grained arc consistency algorithm
Artificial Intelligence
QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Conflict-directed backjumping revisited
Journal of Artificial Intelligence Research
Mendelian error detection in complex pedigrees using weighted constraint satisfaction techniques
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Reasoning from last conflict(s) in constraint programming
Artificial Intelligence
Russian Doll search with tree decomposition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Qualitative CSP, finite CSP, and SAT: comparing methods for qualitative constraint-based reasoning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Exploiting problem structure for solution counting
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Weight-based Heuristics for Constraint Satisfaction and Combinatorial Optimization Problems
Journal of Mathematical Modelling and Algorithms
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In this paper, we propose an approach to guide search to sources of conflicts. The principle is the following: the last variable involved in the last conflict is selected in priority, as long as the constraint network can not be made consistent, in order to find the (most recent) culprit variable, following the current partial instantiation from the leaf to the root of the search tree. In other words, the variable ordering heuristic is violated, until a backtrack to the culprit variable occurs and a singleton consistent value is found. Consequently, this way of reasoning can easily be grafted to many search algorithms and represents an original way to avoid thrashing. Experiments over a wide range of benchmarks demonstrate the effectiveness of this approach.