Semantics for using Stochastic Constraint Solvers in Constraint Logic Programming

This paper proposes a number of models for integrating finitedomain 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.