Maintaining Soft Arc Consistency in BnB-ADOPT+ During Search
Gutierrez and Meseguer show how to enforce consistency in BnB-ADOPT + for distributed constraint optimization, but they consider unconditional deletions only. However, during search, more values can be pruned conditionally according to variable instantiations that define subproblems. Enforcing consi...
| Autores: | , , , , |
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| Tipo de recurso: | otro |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/133453 |
| Acceso en línea: | http://hdl.handle.net/10261/133453 |
| Access Level: | acceso abierto |
| Palabra clave: | Asynchronicity Distributed constraint optimizations Sub-problems Search space reduction Non-trivial tasks Arc consistency |
| Sumario: | Gutierrez and Meseguer show how to enforce consistency in BnB-ADOPT + for distributed constraint optimization, but they consider unconditional deletions only. However, during search, more values can be pruned conditionally according to variable instantiations that define subproblems. Enforcing consistency in these subproblems can cause further search space reduction. We introduce efficient methods to maintain soft arc consistencies in every subproblem during search, a non trivial task due to asynchronicity and induced overheads. Experimental results show substantial benefits on three different benchmarks. © 2013 Springer-Verlag. |
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