The verification problem in MDPs asks whether, for any policy resolving the nondeterminism, the probability that something bad happens is bounded by some given threshold. This verification problem is often overly pessimistic, as the policies it considers may depend on the complete system state. This paper considers the verification problem for partially observable MDPs, in which the policies make their decisions based on (the history of) the observations emitted by the system. We present an abstraction-refinement framework extending previous instantiations of the Lovejoy-approach. Our experiments show that this framework significantly improves the scalability of the approach.
CITATION STYLE
Bork, A., Junges, S., Katoen, J. P., & Quatmann, T. (2020). Verification of Indefinite-Horizon POMDPs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12302 LNCS, pp. 288–304). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59152-6_16
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