Short proofs are narrow—resolution made simple
Journal of the ACM (JACM)
Preprocessing of intractable problems
Information and Computation
Universal Booleanization of Constraint Models
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Decompositions of grammar constraints
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Journal of Artificial Intelligence Research
Knowledge compilation using theory prime implicates
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Circuit complexity and decompositions of global constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Decompositions of all different, global cardinality and related constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Encodings of the SEQUENCE constraint
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Lazy clause generation reengineered
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
A new algorithm for computing theory prime implicates compilations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
On the power of clause-learning SAT solvers as resolution engines
Artificial Intelligence
Clause-learning algorithms with many restarts and bounded-width resolution
Journal of Artificial Intelligence Research
Complexity issues related to propagation completeness
Artificial Intelligence
Generalising Unit-Refutation Completeness and SLUR via Nested Input Resolution
Journal of Automated Reasoning
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When we encode constraints as Boolean formulas, a natural question is whether the encoding ensures a "propagation completeness" property: is the basic unit propagation mechanism able to deduce all the literals that are logically valid? We consider the problem of automatically finding encodings with this property. Our goal is to compile a naïve definition of a constraint into a good, propagation-complete encoding. Well-known Knowledge Compilation techniques from AI can be used for this purpose, but the constraints for which they can produce a polynomial size encoding are few. We show that the notion of empowerment recently introduced in the SAT literature allows producing encodings that are shorter than with previous techniques, sometimes exponentially.