Download PDFOpen PDF in browserA Machine Learning Technique for Hardness Estimation of QFBV SMT Problems10 pages•Published: August 19, 2013AbstractIn this paper we present an approach for measuring the hardness of SMT problems.We present the required features, the statistical hardness model used and the machine learning technique which we used. We apply our method to estimate the hardness of problems in Quantier Free Bit Vector (QFBV) theory and we used four of the contesting solvers in SMT2011 to demonstrate our technique. We have qualitatively expanded some propositional SAT features existing in the literature to directly work on general SMT problem instances without preprocessing. The results show that our work is a promising proof of concept. Keyphrases: machine learning, smt, statistical hardness models In: Pascal Fontaine and Amit Goel (editors). SMT 2012. 10th International Workshop on Satisfiability Modulo Theories, vol 20, pages 57-66.
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