JFLP: Volume 1998, Article 2

The Journal of Functional and Logic Programming

Volume 1998

Article 2

Published by The MIT Press . Copyright 1998 Massachusetts Institute of Technology.

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Semantics for using Stochastic Constraint Solvers in Constraint Logic Programming

Peter Stuckey and Vincent Tam

20 January 1998

Abstract

This paper proposes a number of models for integrating finite-domain stochastic constraint solvers into constraint logic programming systems 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 between 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 that are typically used in constraint logic programming.

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Self citation

    @article{jflp98-02,
      author={Peter Stuckey and Vincent Tam},
      title={Semantics for using Stochastic Constraint Solvers in Constraint Logic Programming},
      journal={Journal of Functional and Logic Programming},
      volume={1998},
      number={2},
      publisher={The MIT Press},
      month={January},
      year={1998}
    }

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