Feedback Control for Statistical Model Checking of Cyber-Physical Systems
We introduce feedback-control statistical system checking (FC-SSC), a new approach to statistical model checking that exploits principles of feedback-control for the analysis of cyber-physical systems (CPS). FC-SSC uses stochastic system identification to learn a CPS model, importance sampling to estimate the CPS state, and importance splitting to control the CPS so that the probability that the CPS satisfies a given property can be efficiently inferred. We illustrate the utility of FC-SSC on two example applications, each of which is simple enough to be easily understood, yet complex enough to exhibit all of FC-SCC’s features. To the best of our knowledge, FC-SSC is the first statistical system checker to efficiently estimate the probability of rare events in realistic CPS applications or in any complex probabilistic program whose model is either not available, or is infeasible to derive through static-analysis techniques.
KeywordsHide Markov Model Model Check Importance Sampling Reachability Analysis Deterministic Finite Automaton
This work was partially supported by the Doctoral Program Logical Methods in Computer Science funded by the Austrian FWF, and the Austrian National Research Network (nr. S 11405-N23 and S 11412-N23) SHiNE funded by the Austrian Science Fund (FWF).
- 1.Code repository. https://ti.tuwien.ac.at/tacas2015/
- 2.Barbara, M., Frédéric, D., Gerhard, R., Alain, L., Frans, J., Thierry, P. (eds.): Parallel Computing: From Multicores and GPU’s to Petascale. Advances in Parallel Computing, vol. 19. IOS Press, Amsterdam (2010). Proceedings of the Conference ParCo 2009, 1–4, September 2009, Lyon, FrancezbMATHGoogle Scholar
- 4.Broy, M., Geisberger, E.: Cyber-physical Systems, Driving Force for Innovation in Mobility, Health, Energy and Production. The National Academy Of Science and Engineering, Acatech (2012)Google Scholar
- 5.Clarke, E., Grumberg, O., Peled, D.: Model Checking. MIT Press, Cambridge (1999)Google Scholar
- 14.Kahn, H., Harris, T.E.: Estimation of particle transmission by random sampling. In: Applied Mathematics, vol. 5 of series 12. National Bureau of Standards (1951)Google Scholar
- 16.Kanungo, T.: UMDHMM tool. http://www.kanungo.com/software/software.html
- 19.Russell, S., Norvig, P., Intelligence, A.: A Modern Approach, 3rd edn. Prentice-Hall, Upper Saddle River (2010)Google Scholar
- 23.Zuliani, P., Baier, C., Clarke, E.: Rare-event verification for stochastic hybrid systems. In: Proceedings of the 15th ACM International Conference on Hybrid Systems: Computation and Control, HSCC 2012, pp. 217–226. ACM (2012)Google Scholar