Abstract
Neural State Classification (NSC) is a recently proposed method for runtime predictive monitoring of Hybrid Automata (HA) using deep neural networks (DNNs). NSC trains a DNN as an approximate reachability predictor that labels a given HA state x as positive if an unsafe state is reachable from x within a given time bound, and labels x as negative otherwise. NSC predictors have very high accuracy, yet are prone to prediction errors that can negatively impact reliability. To overcome this limitation, we present Neural Predictive Monitoring (NPM), a technique based on NSC and conformal prediction that complements NSC predictions with statistically sound estimates of uncertainty. This yields principled criteria for the rejection of predictions likely to be incorrect, without knowing the true reachability values. We also present an active learning method that significantly reduces both the NSC predictor’s error rate and the percentage of rejected predictions. Our approach is highly efficient, with computation times on the order of milliseconds, and effective, managing in our experimental evaluation to successfully reject almost all incorrect predictions.
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Notes
- 1.
The only assumption is exchangeability, a weaker version of the independent and identically distributed assumption.
- 2.
In [21], the PM problem is called “state classification problem”, and its solution a “state classifier”.
- 3.
We will interchangeably use the term “predictor” for the function returning a vector of class likelihoods, and for the function returning the class with highest likelihood.
- 4.
The choice of \(\varDelta \) is not very important, as long as it is symmetric.
- 5.
As opposed to learning a linear combination of confidence and credibility, which is less interpretable.
- 6.
Note indeed that the \(\alpha \)-score of a sample \((x_i,y_i)\) is zero only if h both correctly predicts \(y_i\) and the corresponding class likelihood \(P_h(y_i\mid x_i)\) is 1.
- 7.
Evaluating our rejection criterion reduces to computing two p-values (confidence and credibility). Each p-value is derived by computing a nonconformity score, which requires one execution of the underlying predictor h, and one search over the array of calibration scores.
References
dReal - Networked Water Tank Controllers (2017). http://dreal.github.io/benchmarks/networks/water/
Alur, R.: Formal verification of hybrid systems. In: Proceedings of the Ninth ACM International Conference on Embedded Software (EMSOFT), pp. 273–278, October 2011
Babaee, R., Gurfinkel, A., Fischmeister, S.: Predictive run-time verification of discrete-time reachability properties in black-box systems using trace-level abstraction and statistical learning. In: Colombo, C., Leucker, M. (eds.) RV 2018. LNCS, vol. 11237, pp. 187–204. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03769-7_11
Bak, S., Johnson, T.T., Caccamo, M., Sha, L.: Real-time reachability for verified simplex design. In: Real-Time Systems Symposium (RTSS), 2014 IEEE, pp. 138–148. IEEE (2014)
Balasubramanian, V., Ho, S.S., Vovk, V.: Conformal prediction for reliable machine learning: theory, adaptations and applications. Newnes (2014)
Batuwita, R., Palade, V.: Class imbalance learning methods for support vector machines (2013)
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)
Bortolussi, L., Cairoli, F., Paoletti, N., Stoller, S.D.: Conformal predictions for hybrid system state classification. In: From Reactive Systems to Cyber-Physical Systems, to appear (2019)
Bortolussi, L., Milios, D., Sanguinetti, G.: Smoothed model checking for uncertain continuous-time Markov chains. Inf. Comput. 247, 235–253 (2016)
Chen, X., Sankaranarayanan, S.: Model predictive real-time monitoring of linear systems. In: Real-Time Systems Symposium (RTSS), 2017 IEEE, pp. 297–306. IEEE (2017)
Djeridane, B., Lygeros, J.: Neural approximation of PDE solutions: an application to reachability computations. In: Proceedings of the 45th IEEE Conference on Decision and Control, pp. 3034–3039. IEEE (2006)
Donzé, A., Maler, O.: Robust satisfaction of temporal logic over real-valued signals. In: Chatterjee, K., Henzinger, T.A. (eds.) FORMATS 2010. LNCS, vol. 6246, pp. 92–106. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15297-9_9
Gao, S., Kong, S., Clarke, E.M.: dReal: an SMT solver for nonlinear theories over the reals. In: Bonacina, M.P. (ed.) CADE 2013. LNCS (LNAI), vol. 7898, pp. 208–214. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38574-2_14
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)
Lehmann, E.L., Romano, J.P.: Testing Statistical Hypotheses. Springer Texts in Statistics. Springer, New York (2006)
Makili, L.E., Sánchez, J.A.V., Dormido-Canto, S.: Active learning using conformal predictors: application to image classification. Fusion Sci. Technol. 62(2), 347–355 (2012)
Melluish, T., Saunders, C., Nouretdinov, I., Vovk, V.: The typicalness framework: a comparison with the bayesian approach. University of London, Royal Holloway (2001)
Paoletti, N., Liu, K.S., Smolka, S.A., Lin, S.: Data-driven robust control for type 1 diabetes under meal and exercise uncertainties. In: Feret, J., Koeppl, H. (eds.) CMSB 2017. LNCS, vol. 10545, pp. 214–232. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67471-1_13
Papadopoulos, H.: Inductive conformal prediction: Theory and application to neural networks. In: Tools in artificial intelligence. InTech (2008)
Phan, D., Paoletti, N., Zhang, T., Grosu, R., Smolka, S.A., Stoller, S.D.: Neural state classification for hybrid systems. ArXiv e-prints, July 2018
Phan, D., Paoletti, N., Zhang, T., Grosu, R., Smolka, S.A., Stoller, S.D.: Neural state classification for hybrid systems. In: Lahiri, S.K., Wang, C. (eds.) ATVA 2018. LNCS, vol. 11138, pp. 422–440. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01090-4_25
Qin, X., Deshmukh, J.V.: Predictive monitoring for signal temporal logic with probabilistic guarantees. In: Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control, pp. 266–267. ACM (2019)
Rasmussen, C.E., Williams, C.K.: Gaussian Processes for Machine Learning, vol. 1. MIT press, Cambridge (2006)
Royo, V.R., Fridovich-Keil, D., Herbert, S., Tomlin, C.J.: Classification-based approximate reachability with guarantees applied to safe trajectory tracking. arXiv preprint arXiv:1803.03237 (2018)
Sauter, G., Dierks, H., Fränzle, M., Hansen, M.R.: Lightweight hybrid model checking facilitating online prediction of temporal properties. In: Proceedings of the 21st Nordic Workshop on Programming Theory, pp. 20–22 (2009)
Sha, L.: Using simplicity to control complexity. IEEE Softw. 4, 20–28 (2001)
Vovk, V., Gammerman, A., Shafer, G.: Algorithmic learning in a random world. Springer, Heidelberg (2005)
Acknowledgements
This material is based on work supported in part by NSF Grants CCF-1414078, CCF-1918225, CPS-1446832, and IIS-1447549.
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Bortolussi, L., Cairoli, F., Paoletti, N., Smolka, S.A., Stoller, S.D. (2019). Neural Predictive Monitoring. In: Finkbeiner, B., Mariani, L. (eds) Runtime Verification. RV 2019. Lecture Notes in Computer Science(), vol 11757. Springer, Cham. https://doi.org/10.1007/978-3-030-32079-9_8
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