SATenstein: Automatically building local search SAT solvers from components

Designing high-performance solvers for computationally hard problems is a difficult and often time-consuming task. Although such design problems are traditionally solved by the application of human expertise, we argue instead for the use of automatic methods. In this work, we consider the design of...

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Bibliographic Details
Published in:Artificial intelligence Vol. 232; pp. 20 - 42
Main Authors: KhudaBukhsh, Ashiqur R., Xu, Lin, Hoos, Holger H., Leyton-Brown, Kevin
Format: Journal Article
Language:English
Published: Elsevier B.V 01.03.2016
Subjects:
Algorithms
Automatic algorithm configuration
Automation
Expert systems
SAT
Searching
Solvers
State of the art
Stochastic local search
Strategy
Tasks
Stochastic local search
Automatic algorithm configuration
SAT
ISSN:0004-3702, 1872-7921
Online Access:Get full text
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Summary:Designing high-performance solvers for computationally hard problems is a difficult and often time-consuming task. Although such design problems are traditionally solved by the application of human expertise, we argue instead for the use of automatic methods. In this work, we consider the design of stochastic local search (SLS) solvers for the propositional satisfiability problem (SAT). We first introduce a generalized, highly parameterized solver framework, dubbed SATenstein, that includes components drawn from or inspired by existing high-performance SLS algorithms for SAT. The parameters of SATenstein determine which components are selected and how these components behave; they allow SATenstein to instantiate many high-performance solvers previously proposed in the literature, along with trillions of novel solver strategies. We used an automated algorithm configuration procedure to find instantiations of SATenstein that perform well on several well-known, challenging distributions of SAT instances. Our experiments show that SATenstein solvers achieved dramatic performance improvements as compared to the previous state of the art in SLS algorithms; for many benchmark distributions, our new solvers also significantly outperformed all automatically tuned variants of previous state-of-the-art algorithms.
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ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2015.11.002