Abstract
Symbolic model checking methods have been extended recently to the verification of probabilistic systems. However, the representation of the transition matrix may be expensive for very large systems and may induce a prohibitive cost for the model checking algorithm. In this paper, we propose an approximation method to verify quantitative properties on discrete Markov chains. We give a randomized algorithm to approximate the probability that a property expressed by some positive LTL formula is satisfied with high confidence by a probabilistic system. Our randomized algorithm requires only a succinct representation of the system and is based on an execution sampling method. We also present an implementation and a few classical examples to demonstrate the effectiveness of our approach.
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Hérault, T., Lassaigne, R., Magniette, F., Peyronnet, S. (2004). Approximate Probabilistic Model Checking. In: Steffen, B., Levi, G. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2004. Lecture Notes in Computer Science, vol 2937. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24622-0_8
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DOI: https://doi.org/10.1007/978-3-540-24622-0_8
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