Using Stochastic Solvers in Constraint Logic Programming
Peter J. Stuckey
Department of Computer Science,
The University of Melbourne,
Parkville 3052, Australia.
pjs@cs.mu.oz.au
Vincent W.L. Tam
Department of Computer Science,
The University of Melbourne,
Parkville 3052, Australia.
vtam@cs.mu.oz.au
Abstract
This paper proposes a general framework for integrating a constraint
logic programming system with a stochastic constraint solver 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 kinds of information a stochastic solver provides, and
that which a constraint logic programming system requires.
We study the semantic properties 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 a modified neural
network simulator, GENET, as a constraint solver.
We study various strategies for making use of this
constraint solver, and compare the efficiency
of the system on some sample problems against the propagation based
solver approaches typically used in constraint logic programming.
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