We present novel techniques for automated compositional verification of synchronous probabilistic systems. First, we give an assume-guarantee framework for verifying probabilistic safety properties of systems modelled as discrete-time Markov chains. Assumptions about system components are represented as probabilistic finite automata (PFAs) and the relationship between components and assumptions is captured by weak language inclusion. In order to implement this framework, we develop a semi-algorithm to check language inclusion for PFAs and a new active learning method for PFAs. The latter is then used to automatically generate assumptions for compositional verification. © 2011 Springer-Verlag.
CITATION STYLE
Feng, L., Han, T., Kwiatkowska, M., & Parker, D. (2011). Learning-based compositional verification for synchronous probabilistic systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6996 LNCS, pp. 511–521). https://doi.org/10.1007/978-3-642-24372-1_40
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