Efficient MUS extraction with resolution

Abstract

We report advances in state-of-the-art algorithms for the problem of Minimal Unsatisfiable Subformula (MUS) extraction. First, we demonstrate how to apply techniques used in the past to speed up resolution-based Group MUS extraction to plain MUS extraction. Second, we show that model rotation, presented in the context of assumption-based MUS extraction, can also be used with resolution-based MUS extraction. Third, we introduce an improvement to rotation, called eager rotation. Finally, we propose a new technique for speeding-up resolution-based MUS extraction, called path strengthening. We integrated the above techniques into the publicly available resolution-based MUS extractor HaifaMUC, which, as a result, now outperforms leading MUS extractors.