A sufficient condition for backtrack-bounded search
Journal of the ACM (JACM)
On the parallel complexity of discrete relaxation in constraint satisfaction networks
Artificial Intelligence
Improvements to propositional satisfiability search algorithms
Improvements to propositional satisfiability search algorithms
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Enhancing Davis Putnam with extended binary clause reasoning
Eighteenth national conference on Artificial intelligence
A Comparison between SAT and CSP Techniques
Constraints
Domain filtering consistencies
Journal of Artificial Intelligence Research
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Neighborhood inverse consistency preprocessing
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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
SAT Encoding and CSP Reduction for Interconnected Alldiff Constraints
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Mapping CSP into many-valued SAT
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
The log-support encoding of CSP into SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
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Constraint Satisfaction Problems and Propositional Satisfiability, are frameworks widely used to represent and solve combinatorial problems. A concept of primary importance in both frameworks is that of constraint propagation. In this paper we study and compare the amount of propagation that can be achieved, using various methods, when translating a problem from one framework into another. Our results complement, extend, and tie together recent similar studies. We provide insight as to which translation is preferable, with respect to the strength of propagation in the original problem and the encodings.