TY - GEN

T1 - Models for using stochastic constraint solvers in constraint logic programming

AU - Stuckey, Peter J.

AU - Tam, Vincent

PY - 1996/1/1

Y1 - 1996/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84957639285&partnerID=8YFLogxK

U2 - 10.1007/3-540-61756-6_101

DO - 10.1007/3-540-61756-6_101

M3 - Conference Paper

AN - SCOPUS:84957639285

SN - 3540617566

SN - 9783540617563

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 423

EP - 437

BT - Programming Languages

A2 - Swierstra, S. Doaitse

A2 - Kuchen, Herbert

PB - Springer

T2 - 8th International Symposium on Programming Languages, Implementations, Logics, and Programs, PLILP 1996

Y2 - 24 September 1996 through 27 September 1996

ER -