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Finding Unsatisfiable Subformulas with Stochastic Method

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Intelligent Data Engineering and Automated Learning - IDEAL 2007 (IDEAL 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4881))

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Abstract

Explaining the causes of infeasibility of Boolean formulas has many practical applications in various fields. A small unsatisfiable subformula provides a succinct explanation of infeasibility and is valuable for applications. In recent years the problem of finding unsatisfiable subformulas has been addressed frequently by research works, which are mostly based on the SAT solvers with DPLL backtrack-search algorithm. However little attention has been concentrated on extraction of unsatisfiable subformulas using stochastic methods. In this paper, we propose a resolution-based stochastic local search algorithm to derive unsatisfiable subformulas. This approach directly constructs the resolution sequences for proving unsatisfiability with a local search procedure, and then extracts small unsatisfiable subformulas from the refutation traces. We report and analyze the experimental results on benchmarks.

This work is supported by the National Natural Science Foundation of China under grant No. 60603088.

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Hujun Yin Peter Tino Emilio Corchado Will Byrne Xin Yao

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Zhang, J., Shen, S., Li, S. (2007). Finding Unsatisfiable Subformulas with Stochastic Method. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_40

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  • DOI: https://doi.org/10.1007/978-3-540-77226-2_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77225-5

  • Online ISBN: 978-3-540-77226-2

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