Using Cost Distributions to Guide Weight Decay in Local Search for SAT

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Title Using Cost Distributions to Guide Weight Decay in Local Search for SAT
Author Thornton, John Richard; Pham, Duc Nghia
Publication Title PRICAI 2008: Trends in Artificial Intelligence
Editor Tu-Bao Ho and Zhi-Hua Zhou
Year Published 2008
Place of publication Heidelberg, Germany
Publisher Springer
Abstract Although clause weighting local search algorithms have produced some of the best results on a range of challenging satisfiability (SAT) benchmarks, this performance is dependent on the careful hand-tuning of sensitive parameters. When such hand-tuning is not possible, clause weighting algorithms are generally outperformed by self-tuning WalkSAT-based algorithms such as AdaptNovelty+ and AdaptG2WSAT. In this paper we investigate tuning the weight decay parameter of two clause weighting algorithms using the statistical properties of cost distributions that are dynamically accumulated as the search progresses. This method selects a parameter setting both according to the speed of descent in the cost space and according to the shape of the accumulated cost distribution, where we take the shape to be a predictor of future performance. In a wide ranging empirical study we show that this automated approach to parameter tuning can outperform the default settings for two state-of-the-art algorithms that employ clause weighting (PAWS and gNovelty+). We also show that these self-tuning algorithms are competitive with three of the best-known self-tuning SAT local search techniques: RSAPS, AdaptNovelty+ and AdaptG2WSAT.
Peer Reviewed Yes
Published Yes
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Copyright Statement Copyright 2008 Springer. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at
ISBN 978-3-540-89196-3
Conference name 10th Pacific Rim International Conference on Artificial Intelligence
Location Hanoi, Vietnam
Date From 2008-12-15
Date To 2008-12-19
Date Accessioned 2009-03-11
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Artificial Intelligence and Image Processing
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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