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poster

Predictive monitoring for signal temporal logic with probabilistic guarantees: poster abstract

Published: 16 April 2019 Publication History

Abstract

Monitoring is an effective approach for identifying safety violations for complex cyber-physical systems. In this poster, we consider safety specifications expressed in Signal Temporal Logic (STL). STL is a logic for specifying timed properties of real-valued signals, and there has been significant work on offline and online monitoring of STL formulas on signals. Boolean monitoring techniques solve the problem of determining if a given STL formula is satisfied by a signal, while robust monitoring techniques seek to compute a quantitative degree of satisfaction of the formula. Online techniques can compute satisfaction or violation of the formula when the entire signal is not available, but existing online techniques can only provide worst-case estimates of satisfaction (or violation). In this poster, we propose algorithms to predict the satisfaction or violation of an STL formula when only partial information of a trace (i.e. its prefix) is available. The output of our algorithm is a predicted interval for the robust satisfaction value, along with a probabilistic guarantee on the correctness of the prediction. We demonstrate the utility of our approach on monitoring a safety-critical signal in the context of unmanned aerial vehicle.

References

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Yashwanth Annapureddy, Che Liu, Georgios E Fainekos, and Sriram Sankaranarayanan. 2011. S-TaLiRo: A Tool for Temporal Logic Falsification for Hybrid Systems. In TACAS, Vol. 6605. Springer, 254--257.
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Ezio Bartocci, Luca Bortolussi, and Guido Sanguinetti. 2013. Learning temporal logical properties discriminating ECG models of cardiac arrhytmias. arXiv preprint arXiv:1312.7523 (2013).
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Ezio Bartocci, Luca Bortolussi, and Guido Sanguinetti. 2014. Data-driven statistical learning of temporal logic properties. In Proc. of FORMATS. 23--37.
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Bardh Hoxha, Houssam Abbas, and Georgios Fainekos. 2015. Benchmarks for Temporal Logic Requirements for Automotive Systems. In ARCH14-15. 1st and 2nd International Workshop on Applied veRification for Continuous and Hybrid Systems (EPiC Series in Computing), Goran Frehse and Matthias Althoff (Eds.), Vol. 34. EasyChair, 25--30.
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Xiaoqing Jin, Jyotirmoy V Deshmukh, James Kapinski, Koichi Ueda, and Ken Butts. 2014. Powertrain control verification benchmark. In Proceedings of the 17th international conference on Hybrid systems: computation and control. ACM, 253--262.
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J. Kapinski, X. Jin, J. Deshmukh, A. Donzé, T. Yamaguchi, H. Ito, T. Kaga, S. Kobuna, and S. A. Seshia. 2016. ST-Lib: A Library for Specifying and Classifying Model Behaviors. In SAE Technical Paper. SAE.
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Zhaodan Kong, Austin Jones, Ana Medina Ayala, Ebru Aydin Gol, and Calin Belta. 2014. Temporal logic inference for classification and prediction from data. In Proc. of HSCC. 273--282.
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Xin Qin and Jyotirmoy V. Deshmukh. 2018. Joint Probability Distribution of Prediction Errors of ARIMA. CoRR abs/1811.04685v1 (2018). https://arxiv.org/abs/1811.04685v1

Cited By

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  • (2023)Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic ProcessesProceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control10.1145/3575870.3587113(1-11)Online publication date: 9-May-2023
  • (2023)Learning-Based Approaches to Predictive Monitoring with Conformal Statistical GuaranteesRuntime Verification10.1007/978-3-031-44267-4_26(461-487)Online publication date: 1-Oct-2023
  • (2021)Neural predictive monitoring and a comparison of frequentist and Bayesian approachesInternational Journal on Software Tools for Technology Transfer (STTT)10.1007/s10009-021-00623-123:4(615-640)Online publication date: 1-Aug-2021
  • Show More Cited By

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  1. Predictive monitoring for signal temporal logic with probabilistic guarantees: poster abstract

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            cover image ACM Conferences
            HSCC '19: Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control
            April 2019
            299 pages
            ISBN:9781450362825
            DOI:10.1145/3302504
            Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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            New York, NY, United States

            Publication History

            Published: 16 April 2019

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            Author Tags

            1. monitoring
            2. probabilistic reasoning
            3. signal temporal logic

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            • Poster

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            • This work was funded in part by the US National Science Foundation (NSF)

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            HSCC '19
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            Overall Acceptance Rate 153 of 373 submissions, 41%

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            Cited By

            View all
            • (2023)Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic ProcessesProceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control10.1145/3575870.3587113(1-11)Online publication date: 9-May-2023
            • (2023)Learning-Based Approaches to Predictive Monitoring with Conformal Statistical GuaranteesRuntime Verification10.1007/978-3-031-44267-4_26(461-487)Online publication date: 1-Oct-2023
            • (2021)Neural predictive monitoring and a comparison of frequentist and Bayesian approachesInternational Journal on Software Tools for Technology Transfer (STTT)10.1007/s10009-021-00623-123:4(615-640)Online publication date: 1-Aug-2021
            • (2021)Neural Predictive Monitoring Under Partial ObservabilityRuntime Verification10.1007/978-3-030-88494-9_7(121-141)Online publication date: 11-Oct-2021
            • (2019)Neural Predictive MonitoringRuntime Verification10.1007/978-3-030-32079-9_8(129-147)Online publication date: 8-Oct-2019

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