Computer Science – Artificial Intelligence
Scientific paper
2011-06-30
Journal Of Artificial Intelligence Research, Volume 21, pages 193-243, 2004
Computer Science
Artificial Intelligence
Scientific paper
10.1613/jair.1353
This is the first of three planned papers describing ZAP, a satisfiability engine that substantially generalizes existing tools while retaining the performance characteristics of modern high-performance solvers. The fundamental idea underlying ZAP is that many problems passed to such engines contain rich internal structure that is obscured by the Boolean representation used; our goal is to define a representation in which this structure is apparent and can easily be exploited to improve computational performance. This paper is a survey of the work underlying ZAP, and discusses previous attempts to improve the performance of the Davis-Putnam-Logemann-Loveland algorithm by exploiting the structure of the problem being solved. We examine existing ideas including extensions of the Boolean language to allow cardinality constraints, pseudo-Boolean representations, symmetry, and a limited form of quantification. While this paper is intended as a survey, our research results are contained in the two subsequent articles, with the theoretical structure of ZAP described in the second paper in this series, and ZAP's implementation described in the third.
Dixon H. E.
Ginsberg M. L.
Parkes Andrew J.
No associations
LandOfFree
Generalizing Boolean Satisfiability I: Background and Survey of Existing Work does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Generalizing Boolean Satisfiability I: Background and Survey of Existing Work, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Generalizing Boolean Satisfiability I: Background and Survey of Existing Work will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-479995