Arc and path consistence revisited
Artificial Intelligence
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
New methods to color the vertices of a graph
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Radio Link Frequency Assignment
Constraints
Contradicting Conventional Wisdom in Constraint Satisfaction
PPCP '94 Proceedings of the Second International Workshop on Principles and Practice of Constraint Programming
The Brélaz Heuristic and Optimal Static Orderings
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Neighborhood-Based Variable Ordering Heuristics for the Constraint Satisfaction Problem
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
AC-3d an Efficient Arc-Consistency Algorithm with a Low Space-Complexity
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Experimental studies of variable selection strategies based on constraint weights
Journal of Algorithms
Efficient constraint propagation engines
ACM Transactions on Programming Languages and Systems (TOPLAS)
Domain filtering consistencies
Journal of Artificial Intelligence Research
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Experimental evaluation of preprocessing techniques in constraint satisfaction problems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Look-ahead value ordering for constraint satisfaction problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Using inference to reduce arc consistency computation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An optimal coarse-grained arc consistency algorithm
Artificial Intelligence
Solution counting algorithms for constraint-centered search heuristics
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Sampling strategies and variable selection in weighted degree heuristics
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Some applications of graph bandwidth to constraint satisfaction problems
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Conflict directed variable selection strategies for constraint satisfaction problems
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Fundamenta Informaticae - RCRA 2008 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
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A key factor that can dramatically reduce the search space during constraint solving is the criterion under which the variable to be instantiated next is selected. For this purpose numerous heuristics have been proposed. Some of the best of such heuristics exploit information about failures gathered throughout search and recorded in the form of constraint weights, while others measure the importance of variable assignments in reducing the search space. In this work we experimentally evaluate the most recent and powerful variable ordering heuristics, and new variants of them, over a wide range of benchmarks. Results demonstrate that heuristics based on failures are in general more efficient. Based on this, we then derive new revision ordering heuristics that exploit recorded failures to efficiently order the propagation list when arc consistency is maintained during search. Interestingly, in addition to reducing the number of constraint checks and list operations, these heuristics are also able to cut down the size of the explored search tree.