A method to avoid duplicative flipping in local search for SAT

  • Authors:

  • Thach-Thao Duong;Duc Nghia Pham;Abdul Sattar

  • Affiliations:

  • Queensland Research Laboratory, NICTA, and Institute for Integrated and Intelligent Systems, Griffith University, QLD, Australia;Queensland Research Laboratory, NICTA, and Institute for Integrated and Intelligent Systems, Griffith University, QLD, Australia;Queensland Research Laboratory, NICTA, and Institute for Integrated and Intelligent Systems, Griffith University, QLD, Australia

  • Venue:
  • AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
  • Year:
  • 2012

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Abstract

Stochastic perturbation on variable flipping is the key idea of local search for SAT. Observing that variables are flipped several times in an attempt to escape from a local minimum, this paper presents a duplication learning mechanism in stagnation stages to minimise duplicative variable flipping. The heuristic incorporates the learned knowledge into a variable weighting scheme to effectively prevent the search from selecting duplicative variables. Additionally, probability-based and time window smoothing techniques are adopted to eliminate the effects of redundant information. The integration of the heuristic and gNovelty+ was compared with the original solvers and other state-of-the-art local search solvers. The experimental results showed that the new solver outperformed other solvers on the full set of SAT 2011 competition instances and three sets of real-world verification problems.