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International Conference on Runtime Verification

RV 2016: Runtime Verification pp 404–419Cite as

Decision-Theoretic Monitoring of Cyber-Physical Systems

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10012))

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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Notes

  1. 1.

    Since we are only considering discrete outputs, the probability function becomes a probability distribution in z.

References

  1. Agate, R., Seward, D.: Autonomous safety decision-making in intelligent robotic systems in the uncertain environments. In: Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2008, pp. 1–6. IEEE (2008)

    Google Scholar 

  2. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2–3), 235–256 (2002)

    Article  MATH  Google Scholar 

  3. Bai, H., Hsu, D., Lee, W.S., Ngo, V.A.: Monte Carlo value iteration for continuous-state POMDPs. In: Hsu, D., Isler, V., Latombe, J.-C., Lin, M.C. (eds.) Algorithmic Foundations of Robotics IX. Springer Tracts in Advanced Robotics, vol. 68, pp. 175–191. Springer, Heidelberg (2011). doi:10.1007/978-3-642-17452-0_11

    Chapter  Google Scholar 

  4. Blom, H., Bloem, E.: Particle filtering for stochastic hybrid systems. In: 43rd IEEE Conference on Decision and Control, CDC, vol. 3 (2004)

    Google Scholar 

  5. Clarke, E.M., Grumberg, O., Peled, D.: Model Checking. MIT press, Cambridge (1999)

    Google Scholar 

  6. Henzinger, T.A., Kopke, P.W., Puri, A., Varaiya, P.: What’s decidable about hybrid automata? J. Comput. Syst. Sci. 57(1), 94–124 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  7. Hofbaur, M.W., Williams, B.C.: Mode estimation of probabilistic hybrid systems. In: Tomlin, C.J., Greenstreet, M.R. (eds.) HSCC 2002. LNCS, vol. 2289, pp. 253–266. Springer, Heidelberg (2002). doi:10.1007/3-540-45873-5_21

    Chapter  Google Scholar 

  8. Radu, V.: Application. In: Radu, V. (ed.) Stochastic Modeling of Thermal Fatigue Crack Growth. Applied Condition Monitoring, vol. 1, pp. 63–70. Springer, Switzerland (2015)

    Google Scholar 

  9. Kanade, A., Alur, R., Ivančić, F., Ramesh, S., Sankaranarayanan, S., Shashidhar, K.C.: Generating and analyzing symbolic traces of simulink/stateflow models. In: Bouajjani, A., Maler, O. (eds.) CAV 2009. LNCS, vol. 5643, pp. 430–445. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02658-4_33

    Chapter  Google Scholar 

  10. Koutsoukos, X., Kurien, J., Zhao, F.: Estimation of distributed hybrid systems using particle filtering methods. In: Maler, O., Pnueli, A. (eds.) HSCC 2003. LNCS, vol. 2623, pp. 298–313. Springer, Heidelberg (2003). doi:10.1007/3-540-36580-X_23

    Chapter  Google Scholar 

  11. Kumar, R., Garg, V.: Control of stochastic discrete event systems modeled by probabilistic languages. IEEE Trans. Autom. Control 46(4), 593–606 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  12. Lerner, U., Moses, B., Scott, M., McIlraith, S., Koller, D.: Monitoring a complex physical system using a hybrid dynamic bayes net. In: Proceedings of the 18th Annual Conference on Uncertainty in AI (UAI), pp. 301–310 (2002)

    Google Scholar 

  13. Pantelic, V., Postma, S., Lawford, M.: Probabilistic supervisory control of probabilistic discrete event systems. IEEE Trans. Autom. Control 54(8), 2013–2018 (2009)

    Article  MathSciNet  Google Scholar 

  14. Papadimitriou, C.H., Tsitsiklis, J.N.: The complexity of Markov decision processes. Math. Oper. Res. 12(3), 441–450 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  15. Tabuada, P.: Verification and Control of Hybrid Systems: A Symbolic Approach. Springer Science & Business Media, New York (2009)

    Google Scholar 

  16. Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source Robot Operating System. In: ICRA Workshop on Open Source Software 3 (2009)

    Google Scholar 

  17. Russell, S., Norvig, P., Intelligence, A.: A Modern Approach. Prentice Hall, Englewood (2009)

    Google Scholar 

  18. Seward, D., Pace, C., Agate, R.: Safe and effective navigation of autonomous robots in hazardous environments. Auton. Robot. 22(3), 223–242 (2007)

    Article  Google Scholar 

  19. Silver, D., Veness, J.: Monte-Carlo planning in large POMDPs. In: Advances in Neural Information Processing Systems, pp. 2164–2172 (2010)

    Google Scholar 

  20. Sistla, A.P., Žefran, M., Feng, Y.: Runtime monitoring of stochastic cyber-physical systems with hybrid state. In: Khurshid, S., Sen, K. (eds.) RV 2011. LNCS, vol. 7186, pp. 276–293. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29860-8_21

    Chapter  Google Scholar 

  21. Sistla, A.P., Žefran, M., Feng, Y.: Monitorability of stochastic dynamical systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 720–736. Springer, Heidelberg (2011). doi:10.1007/978-3-642-22110-1_58

    Chapter  Google Scholar 

  22. Sistla, A.P., Žefran, M., Feng, Y., Ben, Y.: Timely monitoring of partially observable stochastic systems. In: Proceedings of the 17th International Conference on Hybrid Systems: Computation and Control, pp. 61–70. ACM (2014)

    Google Scholar 

  23. 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

    Chapter  Google Scholar 

  24. Verma, V., Gordon, G., Simmons, R., Thrun, S.: Real-time fault diagnosis. IEEE Robot. Autom. Mag. 11(2), 56–66 (2004)

    Article  Google Scholar 

  25. Yoo, T., Lafortune, S.: Polynomial-time verification of diagnosability of partially observed discrete-event systems. IEEE Trans. Autom. Control 47(9), 1491–1495 (2002)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. University of Illinois at Chicago, Chicago, USA

    Andrey Yavolovsky, Miloš Žefran & A. Prasad Sistla

Authors
  1. Andrey Yavolovsky
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  2. Miloš Žefran
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  3. A. Prasad Sistla
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Corresponding author

Correspondence to Miloš Žefran .

Editor information

Editors and Affiliations

  1. Université Grenoble Alpes, Inria, Grenoble, France

    Yliès Falcone

  2. IMDEA Software Institute, Madrid, Spain

    César Sánchez

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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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  • DOI: https://doi.org/10.1007/978-3-319-46982-9_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46981-2

  • Online ISBN: 978-3-319-46982-9

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