Feedback Control for Statistical Model Checking of Cyber-Physical Systems

  • K. Kalajdzic
  • C. Jegourel
  • A. Lukina
  • E. Bartocci
  • A. Legay
  • S. A. Smolka
  • R. Grosu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9952)

Abstract

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.

References

  1. 1.
  2. 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, FranceMATHGoogle Scholar
  3. 3.
    Bartocci, E., Grosu, R., Karmarkar, A., Smolka, S.A., Stoller, S.D., Zadok, E., Seyster, J.: Adaptive runtime verification. In: Qadeer, S., Tasiran, S. (eds.) RV 2012. LNCS, vol. 7687, pp. 168–182. Springer, Heidelberg (2013). doi:10.1007/978-3-642-35632-2_18 CrossRefGoogle Scholar
  4. 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. 5.
    Clarke, E., Grumberg, O., Peled, D.: Model Checking. MIT Press, Cambridge (1999)Google Scholar
  6. 6.
    Clarke, E.M., Zuliani, P.: Statistical model checking for cyber-physical systems. In: Bultan, T., Hsiung, P.-A. (eds.) ATVA 2011. LNCS, vol. 6996, pp. 1–12. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24372-1_1 CrossRefGoogle Scholar
  7. 7.
    Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, New York (2001)CrossRefMATHGoogle Scholar
  8. 8.
    Duflot, M., Fribourg, L., Picaronny, C.: Randomized dining philosophers without fairness assumption. Distrib. Comput. 17(1), 65–76 (2004)CrossRefGoogle Scholar
  9. 9.
    Glasserman, P., Heidelberger, P., Shahabuddin, P., Zajic, T.: Multilevel Splitting for Estimating Rare Event Probabilities. Oper. Res. 47(4), 585–600 (1999)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Grosu, R., Smolka, S.A.: Monte Carlo model checking. In: Halbwachs, N., Zuck, L.D. (eds.) TACAS 2005. LNCS, vol. 3440, pp. 271–286. Springer, Heidelberg (2005). doi:10.1007/978-3-540-31980-1_18 CrossRefGoogle Scholar
  11. 11.
    Jegourel, C., Legay, A., Sedwards, S.: Cross-entropy optimisation of importance sampling parameters for statistical model checking. In: Madhusudan, P., Seshia, S.A. (eds.) CAV 2012. LNCS, vol. 7358, pp. 327–342. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31424-7_26 CrossRefGoogle Scholar
  12. 12.
    Jegourel, C., Legay, A., Sedwards, S.: Importance splitting for statistical model checking rare properties. In: Sharygina, N., Veith, H. (eds.) CAV 2013. LNCS, vol. 8044, pp. 576–591. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39799-8_38 CrossRefGoogle Scholar
  13. 13.
    Jegourel, C., Legay, A., Sedwards, S.: An effective heuristic for adaptive importance splitting in statistical model checking. In: Margaria, T., Steffen, B. (eds.) ISoLA 2014. LNCS, vol. 8803, pp. 143–159. Springer, Heidelberg (2014). doi:10.1007/978-3-662-45231-8_11 Google Scholar
  14. 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
  15. 15.
    Kalajdzic, K., Bartocci, E., Smolka, S.A., Stoller, S.D., Grosu, R.: Runtime verification with particle filtering. In: Legay, A., Bensalem, S. (eds.) RV 2013. LNCS, vol. 8174, pp. 149–166. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40787-1_9 CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Rabiner, L.: A tutorial on hidden Markov models, selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  18. 18.
    Roweis, S., Ghahramani, Z.: A unifying review of linear gaussian models. Neural Comput. 11(2), 305–345 (1999)CrossRefGoogle Scholar
  19. 19.
    Russell, S., Norvig, P., Intelligence, A.: A Modern Approach, 3rd edn. Prentice-Hall, Upper Saddle River (2010)Google Scholar
  20. 20.
    Stoller, S.D., Bartocci, E., Seyster, J., Grosu, R., Havelund, K., Smolka, S.A., Zadok, E.: Runtime verification with state estimation. In: Khurshid, S., Sen, K. (eds.) RV 2011. LNCS, vol. 7186, pp. 193–207. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29860-8_15 CrossRefGoogle Scholar
  21. 21.
    Verma, V., Gordon, G., Simmons, R., Thrun, S.: Real-time fault diagnosis [robot fault diagnosis]. IEEE Robot. Autom. Mag. 11(2), 56–66 (2004)CrossRefGoogle Scholar
  22. 22.
    Younes, H., Kwiatkowska, M., Norman, G., Parker, D.: Numerical vs. statistical probabilistic model checking. STTT 8(3), 216–228 (2006)CrossRefMATHGoogle Scholar
  23. 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

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • K. Kalajdzic
    • 1
  • C. Jegourel
    • 4
  • A. Lukina
    • 1
  • E. Bartocci
    • 1
  • A. Legay
    • 2
  • S. A. Smolka
    • 3
  • R. Grosu
    • 1
  1. 1.Vienna University of TechnologyViennaAustria
  2. 2.INRIA Rennes, Bretagne AtlantiqueRennesFrance
  3. 3.Stony Brook UniversityNew YorkUSA
  4. 4.National University of SingaporeSingaporeSingapore

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