Abstract
We present an importance sampling framework that combines symbolic analysis and simulation to estimate the probability of rare reachability properties in stochastic timed automata. By means of symbolic exploration, our framework first identifies states that cannot reach the goal. A state-wise change of measure is then applied on-the-fly during simulations, ensuring that dead ends are never reached. The change of measure is guaranteed by construction to reduce the variance of the estimator with respect to crude Monte Carlo, while experimental results demonstrate that we can achieve substantial computational gains.
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Notes
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One symbolic state may appear in several quadruples.
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Details can be found at http://people.cs.aau.dk/~marius/stratego/rare.html.
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Acknowledgements
This research has received funding from the Sino-Danish Basic Research Centre, IDEA4CPS, funded by the Danish National Research Foundation and the National Science Foundation, China, the Innovation Fund Denmark centre DiCyPS, as well as the ERC Advanced Grant LASSO. Other funding has been provided by the Self Energy-Supporting Autonomous Computation (SENSATION) and Collective Adaptive Systems Synthesis with Non-zero-sum Games (CASSTING) European FP7-ICT projects, and the Embedded Multi-Core systems for Mixed Criticality (EMC2) ARTEMIS project.
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Jegourel, C., Larsen, K.G., Legay, A., Mikučionis, M., Poulsen, D.B., Sedwards, S. (2016). Importance Sampling for Stochastic Timed Automata. In: Fränzle, M., Kapur, D., Zhan, N. (eds) Dependable Software Engineering: Theories, Tools, and Applications. SETTA 2016. Lecture Notes in Computer Science(), vol 9984. Springer, Cham. https://doi.org/10.1007/978-3-319-47677-3_11
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