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Learning Nonlinear Hybrid Automata from Input–Output Time-Series Data

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Automated Technology for Verification and Analysis (ATVA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14215))

Abstract

Learning an automaton that approximates the behavior of a black-box system is a long-studied problem. Besides its theoretical significance, its application to search-based testing and model understanding is recently recognized. We present an algorithm to learn a nonlinear hybrid automaton (HA) that approximates a black-box hybrid system (HS) from a set of input–output traces generated by the HS. Our method is novel in handling (1) both exogenous and endogenous HS and (2) HA with reset associated with each transition. To our knowledge, ours is the first method that achieves both features. We applied our algorithm to various benchmarks and confirmed its effectiveness.

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Notes

  1. 1.

    Notice that, as mentioned above, we only consider the points within a segment that satisfy \(rd( fwd _i, bwd _i) \le \varepsilon _{\textrm{FwdBwd}}\).

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Acknowledgements

We are grateful to the anonymous reviewers for their valuable comments. This work was partially supported by JST CREST Grant No. JPMJCR2012, JST PRESTO Grant No. JPMJPR22CA, JST ACT-X Grant No. JPMJAX200U, and JSPS KAKENHI Grant No. 22K17873 & 19H04084.

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Authors and Affiliations

  1. Graduate School of Informatics, Kyoto University, Kyoto, Japan

    Amit Gurung, Masaki Waga & Kohei Suenaga

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  1. Amit Gurung
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  2. Masaki Waga
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  3. Kohei Suenaga
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Correspondence to Amit Gurung .

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Editors and Affiliations

  1. Université Sorbonne Paris Nord, Villetaneuse, France

    Étienne André

  2. Singapore Management University, Singapore, Singapore

    Jun Sun

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Gurung, A., Waga, M., Suenaga, K. (2023). Learning Nonlinear Hybrid Automata from Input–Output Time-Series Data. In: André, É., Sun, J. (eds) Automated Technology for Verification and Analysis. ATVA 2023. Lecture Notes in Computer Science, vol 14215. Springer, Cham. https://doi.org/10.1007/978-3-031-45329-8_2

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  • DOI: https://doi.org/10.1007/978-3-031-45329-8_2

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