Boosting complete techniques thanks to local search methods

被引:44
作者
Mazure, B [1 ]
Sais, L [1 ]
Gregoire, E [1 ]
机构
[1] Univ Artois, CRIL, F-62307 Lens, France
关键词
D O I
10.1023/A:1018999721141
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:319 / 331
页数:13
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