Abstract
We propose a purely implicit solution to the contextual assumption generation problem in assume-guarantee reasoning. Instead of improving the L* algorithm - a learning algorithm for finite automata, our algorithm computes implicit representations of contextual assumptions by the CDNF algorithm - a learning algorithm for Boolean functions. We report three parametrized test cases where our solution outperforms the monolithic interpolation-based Model Checking algorithm. © 2010 Springer-Verlag.
Cite
CITATION STYLE
Chen, Y. F., Clarke, E. M., Farzan, A., Tsai, M. H., Tsay, Y. K., & Wang, B. Y. (2010). Automated assume-guarantee reasoning through implicit learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6174 LNCS, pp. 511–526). https://doi.org/10.1007/978-3-642-14295-6_44
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