Abstract
We present highly accurate deterministic, probabilistic and hybrid methods for monitoring the full range of ω-regular properties, specified as Streett automata, of stochastic systems modeled as Hidden Markov Chains. The deterministic algorithms employ timeouts that are set dynamically to achieve desired accuracy. The probabilistic algorithms employ coin tossing and can give highly accurate monitors when the system behavior is not known. The hybrid algorithms combine both these techniques. The monitoring algorithms have been implemented as a tool. The tool takes a high level description of an application with probabilities and also a Streett automaton that specifies the property to be monitored. It generates a monitor for monitoring computations of the application. Experimental results comparing the effectiveness of the different algorithms are presented.
This research is partly supported by the NSF grants CCF-0742686 and CCR-0205365.
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Gondi, K., Patel, Y., Sistla, A.P. (2008). Monitoring the Full Range of ω-Regular Properties of Stochastic Systems. In: Jones, N.D., Müller-Olm, M. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2009. Lecture Notes in Computer Science, vol 5403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93900-9_12
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DOI: https://doi.org/10.1007/978-3-540-93900-9_12
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