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AI
2010
Springer

Extended clause learning

14 years 17 days ago
Extended clause learning
The past decade has seen clause learning as the most successful algorithm for SAT instances arising from real-world applications. This practical success is accompanied by theoretical results showing clause learning as equivalent in power to resolution. There exist, however, problems that are intractable for resolution, for which clause-learning solvers are hence doomed. In this paper, we present extended clause learning, a practical SAT algorithm that surpasses resolution in power. Indeed, we prove that it is equivalent in power to extended resolution, a proof system strictly more powerful than resolution. Empirical results based on an initial implementation suggest that the additional theoretical power can indeed translate into substantial practical gains.
Jinbo Huang
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2010
Where AI
Authors Jinbo Huang
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