Abstract
We compare two learning algorithms for generating contextual assumptions in automated assume-guarantee reasoning. The CDNF algorithm implicitly represents contextual assumptions by a conjunction of DNF formulae, while the OBDD learning algorithm uses ordered binary decision diagrams as its representation. Using these learning algorithms, the performance of assume-guarantee reasoning is compared with monolithic interpolation-based Model Checking in parametrized hardware test cases.
This research was sponsored by the GSRC under contract no. 1041377 (Princeton University), National Science Foundation under contracts no. CCF0429120, no. CNS0926181, no. CCF0541245, and no. CNS0931985, Semiconductor Research Corporation under contract no. 2005TJ1366, General Motors under contract no. GMCMUCRLNV301, Air Force (Vanderbilt University) under contract no. 18727S3, the Office of Naval Research under award no. N000141010188, the National Science Council of Taiwan projects no. NSC97-2221-E-001-003-MY3, no. NSC97-2221-E-001-006-MY3, no. NSC97-2221-E-002-074-MY3, and no. NSC99-2218-E-001-002-MY3, Natural Sciences and Engineering Research Council of Canada NSERC Discovery Award, Chinese National 973 Plan under grant no. 2010CB328003, the NSF of China under grants no. 60635020, 60903030 and 90718039, the FORMES Project within LIAMA Consortium, and the French ANR project SIVES ANR-08-BLAN-0326-01.
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Chen, YF. et al. (2010). Comparing Learning Algorithms in Automated Assume-Guarantee Reasoning. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification, and Validation. ISoLA 2010. Lecture Notes in Computer Science, vol 6415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16558-0_52
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