Constraint satisfaction from a deductive viewpoint (Research Note)
Artificial Intelligence
Artificial Intelligence
The OPL optimization programming language
The OPL optimization programming language
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Logic programs with stable model semantics as a constraint programming paradigm
Annals of Mathematics and Artificial Intelligence
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CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Compiling problem specification into SAT
Artificial Intelligence - Special volume on reformulation
Automated reformulation of specifications by safe delay of constraints
Artificial Intelligence
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
Constraint satisfaction, databases, and logic
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
CGRASS: a system for transforming constraint satisfaction problems
ERCIM'02/CologNet'02 Proceedings of the 2002 Joint ERCIM/CologNet international conference on Constraint solving and constraint logic programming
Detecting and breaking symmetries by reasoning on problem specifications
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Exploiting functional dependencies in declarative problem specifications
Artificial Intelligence
STATE OF APPLICATIONS IN AI RESEARCHES FROM AI*IA 2005
Applied Artificial Intelligence
A unifying framework for structural properties of CSPs: definitions, complexity, tractabilit
Journal of Artificial Intelligence Research
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The efficiency of systems for constraint programming (CP) is currently highly affected by the actual formulation of the input problem. To this end, several choices have to be made by modelers in order to write efficient specifications and handle instances of realistic size, and this, of course, represents a major obstacle to reach full declarativeness. Several structural properties of problem specifications have been investigated in order to provide techniques that reformulate a constraint program into one which is more efficiently evaluable by the solver at hand. In this paper we consider two such properties, symmetries and functional dependencies among variables, and show that, by characterizing problem specifications as logical formulae, the task of deciding whether such properties hold, and consequently that of performing the relevant reformulations, can be practically mechanized by means of automated theorem proving (ATP) technology. In particular, we report the results on using ATP technology for checking the existence of symmetries, checking whether a given constraint is symmetry-breaking, and checking the existence of functional dependencies in a specification. The output of the reasoning phase is a transformed constraint program, consisting in a reformulated specification and, possibly, a search strategy. We show our techniques on problems such as graph coloring, Sailco inventory, and protein folding.