New methods to color the vertices of a graph
Communications of the ACM
Generating Satisfiable Problem Instances
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Recovering and Exploiting Structural Knowledge from CNF Formulas
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Tradeoffs in the complexity of backdoor detection
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Advisors for incremental propagation
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Effect of DisCSP variable-ordering heuristics in scale-free networks
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
Effect of DisCSP variable-ordering heuristics in scale-free networks
Multiagent and Grid Systems - Principles and Practice of Multi-Agent Systems
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In this work, our objective is to heuristically discover a simplified form of functional dependencies between variables called weak dependencies. Once discovered, these relations are used to rank the variables. Our method shows that these relations can be detected with some acceptable overhead during constraint propagation. More precisely, each time a variable y gets instantiated as a result of the instantiation of x, a weak dependency (x, y) is recorded. As a consequence, the weight of x is raised, and the variable becomes more likely to be selected by the variable ordering heuristic. Experiments on a large set of problems show that on the average, the search trees are reduced by a factor 3 while runtime is decreased by 31% when compared against dom-wdeg, one of the best dynamic variable ordering heuristic.