Abstract
This paper proposes a number of models for integrating stochastic constraint solvers into constraint logic programming systems in order to solve constraint satisfaction problems efficiently. Stochastic solvers can solve hard constraint satisfaction problems very efficiently, and constraint logic programming allows heuristics and problem breakdown to be encoded in the same language as the constraints. Hence their combination is attractive. Unfortunately there is a mismatch in the kind of information a stochastic solver provides, and that which a constraint logic programming system requires. We study the semantic properties of the various models of constraint logic programming systems that make use of stochastic solvers, and give soundness and completeness results for their use. We describe an example system we have implemented using a modified neural network simulator, GENET, as a constraint solver. We briefly compare the efficiency of these models against the propagation based solver approaches typically used in constraint logic programming.
Keywords
- Constraint Satisfaction Problem
- Usage Strategy
- Derivation Step
- Constraint Solver
- Constraint Logic Programming
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 1996 Springer-Verlag Berlin Heidelberg
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Stuckey, P.J., Tam, V. (1996). Models for using stochastic constraint solvers in constraint logic programming. In: Kuchen, H., Doaitse Swierstra, S. (eds) Programming Languages: Implementations, Logics, and Programs. PLILP 1996. Lecture Notes in Computer Science, vol 1140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61756-6_101
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DOI: https://doi.org/10.1007/3-540-61756-6_101
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