Weight-Enhanced Diversification in Stochastic Local Search for Satisfiability

Paper


Duong, Thach-Thao, Pham, Duc Nghia, Sattar, Abdul and Newton, M. A. Hakim. 2013. "Weight-Enhanced Diversification in Stochastic Local Search for Satisfiability." Rossi, Francesca (ed.) 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013). Beijing, China 03 - 09 Aug 2013
Paper/Presentation Title

Weight-Enhanced Diversification in Stochastic Local Search for Satisfiability

Presentation TypePaper
AuthorsDuong, Thach-Thao (Author), Pham, Duc Nghia (Author), Sattar, Abdul (Author) and Newton, M. A. Hakim (Author)
EditorsRossi, Francesca
Journal or Proceedings TitleProceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)
ERA Conference ID43623
Number of Pages7
Year2013
ISBN9781577356332
Web Address (URL) of Paperhttps://www.iospress.com/catalog/books/twelfth-scandinavian-conference-on-artificial-intelligence
Conference/Event23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)
International Joint Conference on Artificial Intelligence
Event Details
International Joint Conference on Artificial Intelligence
IJCAI
Rank
A
A
A
A
A
Event Details
23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)
Event Date
03 to end of 09 Aug 2013
Event Location
Beijing, China
Abstract

Intensification and diversification are the key factors that control the performance of stochastic local search in satisfiability (SAT). Recently, Novelty Walk has become a popular method for improving diversification of the search and so has been integrated in many well-known SAT solvers such as TNM and gNovelty+. In this paper, we introduce new heuristics to improve the effectiveness of Novelty Walk in terms of reducing search stagnation. In particular, we use weights (based on statistical information collected during the search) to focus the diversification phase onto specific areas of interest. With a given probability, we select the most frequently unsatisfied clause instead of a totally random one as Novelty Walk does. Amongst all the variables appearing in the selected clause, we then select the least flipped variable for the next move. Our experimental results show that the new weight-enhanced diversification method significantly improves the performance of gNovelty+ and thus outperforms other local search SAT solvers on a wide range of structured and random satisfiability benchmarks.

KeywordsArtificial intelligence; Benchmarking; Stochastic systems
ANZSRC Field of Research 2020460210. Satisfiability and optimisation
Byline AffiliationsGriffith University
Institution of OriginUniversity of Southern Queensland
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https://research.usq.edu.au/item/q7115/weight-enhanced-diversification-in-stochastic-local-search-for-satisfiability

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