Abstract
We propose a framework for monitoring and updating, at run-time, the probabilities of temporal properties of stochastic timed automata. Our method is based on Bayesian networks and can be useful in various real-time applications, such as flight control systems and cardiac pacemakers. The framework has been implemented by exploiting the statistical model checking engine of
. By run-time monitoring a set of interesting temporal properties of a given stochastic automaton we update their probabilities, modeled through a Bayesian Network. The main advantages of our method are the capacity to discover non-trivial dependencies between properties and to efficiently update probabilities of unobserved properties given real-time observations. We present empirical results on three application scenarios, showing that the query time can keep up with the speed of some realistic real-time applications. We also present experiments demonstrating that the Bayesian Network approach performance-wise enables run-time monitoring while maintaining or even increasing the accuracy of probability estimation compared to statistical model checking.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
All the experiments ran on an Apple MacBook Pro Mid 2015, 2.5 GHz Quad-Core Intel Core i7, with 16 GB of RAM and only one core has been used.
References
Aceto, L., Bouyer, P., Burgueño, A., Larsen, K.G.: The power of reachability testing for timed automata. In: Arvind, V., Ramanujam, S. (eds.) FSTTCS 1998. LNCS, vol. 1530, pp. 245–256. Springer, Heidelberg (1998). https://doi.org/10.1007/978-3-540-49382-2_22
AlTurki, M., Meseguer, J.: PVeStA: a parallel statistical model checking and quantitative analysis tool. In: Corradini, A., Klin, B., Cîrstea, C. (eds.) CALCO 2011. LNCS, vol. 6859, pp. 386–392. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22944-2_28
Alur, R., Dill, D.L.: A theory of timed automata. Theor. Comput. Sci. 126(2), 183–235 (1994). https://doi.org/10.1016/0304-3975(94)90010-8
Alur, R., Giacobbe, M., Henzinger, T.A., Larsen, K.G., Mikučionis, M.: Continuous-time models for system design and analysis. In: Steffen, B., Woeginger, G. (eds.) Computing and Software Science. LNCS, vol. 10000, pp. 452–477. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91908-9_22
Bulychev, P., et al.: UPPAAL-SMC: statistical model checking for priced timed automata. arXiv preprint arXiv:1207.1272 (2012)
Bulychev, P., David, A., Larsen, K.G., Legay, A., Li, G., Poulsen, D.B.: Rewrite-based statistical model checking of WMTL. In: Qadeer, S., Tasiran, S. (eds.) RV 2012. LNCS, vol. 7687, pp. 260–275. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35632-2_25
Chickering, D.M., Heckerman, D., Meek, C.: Large-sample learning of Bayesian networks is np-hard. J. Mach. Learn. Res. 5(Oct), 1287–1330 (2004)
Chickering, D.M., Meek, C.: Finding optimal Bayesian networks. In: Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, pp. 94–102 (2002)
David, A., Jensen, P.G., Larsen, K.G., Mikučionis, M., Taankvist, J.H.: Uppaal stratego. In: Baier, C., Tinelli, C. (eds.) TACAS 2015. LNCS, vol. 9035, pp. 206–211. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46681-0_16
David, A., Larsen, K.G., Legay, A., Mikučionis, M., Poulsen, D.B.: Uppaal SMC tutorial. Int. J. Softw. Tools Technol. Transf. 17(4), 397–415 (2015). https://doi.org/10.1007/s10009-014-0361-y
David, A., et al.: Statistical model checking for networks of priced timed automata. In: Fahrenberg, U., Tripakis, S. (eds.) FORMATS 2011. LNCS, vol. 6919, pp. 80–96. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24310-3_7
David, A., Larsen, K.G., Legay, A., Mikučionis, M., Wang, Z.: Time for statistical model checking of real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 349–355. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22110-1_27
Feng, Y., Katoen, J.-P., Li, H., Xia, B., Zhan, N.: Monitoring CTMCs by multi-clock timed automata. In: Chockler, H., Weissenbacher, G. (eds.) CAV 2018. LNCS, vol. 10981, pp. 507–526. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96145-3_27
Jegourel, C., Legay, A., Sedwards, S.: A platform for high performance statistical model checking – PLASMA. In: Flanagan, C., König, B. (eds.) TACAS 2012. LNCS, vol. 7214, pp. 498–503. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28756-5_37
Jiang, Z., Pajic, M., Moarref, S., Alur, R., Mangharam, R.: Modeling and verification of a dual chamber implantable pacemaker. In: Flanagan, C., König, B. (eds.) TACAS 2012. LNCS, vol. 7214, pp. 188–203. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28756-5_14
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). https://doi.org/10.1007/978-3-642-40787-1_9
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)
Larsen, K.G., Mikučionis, M., Taankvist, J.H.: Safe and optimal adaptive cruise control. In: Meyer, R., Platzer, A., Wehrheim, H. (eds.) Correct System Design. LNCS, vol. 9360, pp. 260–277. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23506-6_17
Larsen, K.G., Pettersson, P., Yi, W.: UPPAAL in a nutshell. Int. J. Softw. Tools Technol. Transf. 1(1–2), 134–152 (1997). https://doi.org/10.1007/s100090050010
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall Press, New York (2009)
Schwarz, G., et al.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)
Taskesen, E.: bnlearn (2019). https://github.com/erdogant/bnlearn
Wu, X., Ling, H., Dong, Y.: On modeling and verifying of application protocols of TTCAN in flight-control system with UPPAAL. In: 2009 International Conference on Embedded Software and Systems, pp. 572–577. IEEE (2009)
Zhang, N.L., Poole, D.: A simple approach to Bayesian network computations. In: Proceedings of the Biennial Conference-Canadian Society for Computational Studies of Intelligence, pp. 171–178. Canadian Information Processing Society (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Jaeger, M., Larsen, K.G., Tibo, A. (2020). From Statistical Model Checking to Run-Time Monitoring Using a Bayesian Network Approach. In: Deshmukh, J., Ničković, D. (eds) Runtime Verification. RV 2020. Lecture Notes in Computer Science(), vol 12399. Springer, Cham. https://doi.org/10.1007/978-3-030-60508-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-60508-7_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60507-0
Online ISBN: 978-3-030-60508-7
eBook Packages: Computer ScienceComputer Science (R0)