Towards Fewer Parameters for SAT Clause Weighting Algorithms
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Considerable progress has recently been made in using clause weighting algorithms such as DLM and SDF to solve SAT benchmark problems. While these algorithms have outperformed earlier stochastic techniques on many larger problems, this improvement has been bought at the cost of extra parameters and the complexity of fine tuning these parameters to obtain optimal run-time performance. This paper examines the use of parameters, specifically in relation to DLM, to identify underlying features in clause weighting that can be used to eliminate or predict workable parameter settings. To this end we propose and empirically evaluate a simplified clause weighting algorithm that replaces the tabu list and flat moves parameter used in DLM. From this we show that our simplified clause weighting algorithm is competitive with DLM on the four categories of SAT problem for whichDLMhas already been optimised.
AI 2002: Advances in Artificial Intelligence
Copyright 2002 Springer-Verlag. 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 www.springerlink.com