Abstract
Runtime monitoring has been proposed as an alternative to formal verification for safety critical systems. This paper introduces a decision-theoretic view of runtime monitoring. We formulate the monitoring problem as a Partially Observable Markov Decision Process (POMDP). Furthermore, we adopt a Partially Observable Monte-Carlo Planning (POMCP) to compute an approximate optimal policy of the monitoring POMDP. We show how to construct the POMCP for the monitoring problem and demonstrate experimentally that it can be effectively applied even when some of the state-space variables are continuous, the case where many other POMDP solvers fail. Experimental results on a mobile robot system show the effectiveness of the proposed POMDP-monitor.
This research was supported in part by NSF grants CNS-0910988, CNS-1035914, CCF-1319754 and CNS-1314485.
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- 1.
Since we are only considering discrete outputs, the probability function becomes a probability distribution in z.
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Yavolovsky, A., Žefran, M., Sistla, A.P. (2016). Decision-Theoretic Monitoring of Cyber-Physical Systems. In: Falcone, Y., Sánchez, C. (eds) Runtime Verification. RV 2016. Lecture Notes in Computer Science(), vol 10012. Springer, Cham. https://doi.org/10.1007/978-3-319-46982-9_25
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