A Study of Local Minimum Avoidance Heuristics for SAT

Duong, Thach-Thao ORCID: https://orcid.org/0000-0003-2294-3619 and Pham, Duc Nghia and Sattar, Abdul (2012) A Study of Local Minimum Avoidance Heuristics for SAT. In: 20th European Conference on Artificial Intelligence (ECAI 2012), 27 Aug - 31 Aug 2012, Montpellier, France.

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Abstract

Stochastic local search for satisfiability (SAT) has successfully been applied to solve a wide range of problems. However, it still suffers from a major shortcoming, i.e. being trapped in local minima. In this study, we explore different heuristics to avoid local minima. The main idea is to proactively avoid local minima rather than reactively escape from them. This is worthwhile because it is time consuming to successfully escape from a local minimum in a deep and wide valley. In addition, revisiting an encountered local minimum several times makes it worse. Our new trap avoidance heuristics that operate in two phases: (i) learning of pseudo-conflict information at each local minimum, and (ii) using this information to avoid revisiting the same local minimum. We present a detailed empirical study of different strategies to collect pseudo-conflict information (using either static or dynamic heuristics) as well as to forget the outdated information (using naive or time window smoothing). We select a benchmark suite that includes all random and structured instances used in the 2011 SAT competition and three sets of hardware and software verification problems. Our results show that the new heuristics significantly outperform existing stochastic local search solvers (including Sparrow2011 - the best local search solver for random instances in the 2011 SAT competition) on all tested benchmarks.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 30 Mar 2022 05:37
Last Modified: 30 Mar 2022 05:37
Uncontrolled Keywords: Artificial intelligence; Local search (optimization); Stochastic systems; Verification
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460210 Satisfiability and optimisation
Identification Number or DOI: https://doi.org/10.3233/978-1-61499-098-7-300
URI: http://eprints.usq.edu.au/id/eprint/46980

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