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Artificial Intelligence
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The OPL optimization programming language
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Constraints
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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
LPAR'10 Proceedings of the 16th international conference on Logic for programming, artificial intelligence, and reasoning
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CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Boolean equi-propagation for concise and efficient SAT encodings of combinatorial problems
Journal of Artificial Intelligence Research
Breaking symmetries in graph representation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present BEE, a compiler which enables to encode finite domain constraint problems to CNF. Using BEE both eases the encoding process for the user and also performs transformations to simplify constraints and optimize their encoding to CNF. These optimizations are based primarily on equi-propagation and on partial evaluation, and also on the idea that a given constraint may have various possible CNF encodings. Often, the better encoding choice is made after constraint simplification. BEE is written in Prolog and integrates directly with a SAT solver through a suitable Prolog interface. We demonstrate that constraint simplification is often highly beneficial when solving hard finite domain constraint problems. A BEE implementation is available with this paper.