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Boosting complete techniques thanks to local search methods

Boosting complete techniques thanks to local search methods In this paper, an efficient heuristic allowing one to localize inconsistent kernels in propositional knowledge‐bases is described. Then, it is shown that local search techniques can boost the performance of logically complete methods for SAT. More precisely, local search techniques can be used to guide the branching strategy of logically complete techniques like Davis and Putnam's one, giving rise to significant performance improvements, in particular when addressing locally inconsistent problems. Moreover, this approach appears very competitive in the context of consistent SAT instances, too. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Boosting complete techniques thanks to local search methods

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Publisher
Springer Journals
Copyright
Copyright © 1998 by Kluwer Academic Publishers
Subject
Computer Science; Computer Science, general; Artificial Intelligence (incl. Robotics); Mathematics, general; Complexity
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1023/A:1018999721141
Publisher site
See Article on Publisher Site

Abstract

In this paper, an efficient heuristic allowing one to localize inconsistent kernels in propositional knowledge‐bases is described. Then, it is shown that local search techniques can boost the performance of logically complete methods for SAT. More precisely, local search techniques can be used to guide the branching strategy of logically complete techniques like Davis and Putnam's one, giving rise to significant performance improvements, in particular when addressing locally inconsistent problems. Moreover, this approach appears very competitive in the context of consistent SAT instances, too.

Journal

Annals of Mathematics and Artificial IntelligenceSpringer Journals

Published: Oct 4, 2004

References