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Generation and verification of learned stochastic automata using k-NN and statistical model checking

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Abstract

Deriving an accurate behavior model from historical data of a black box for verification and feature forecasting is seen by industry as a challenging issue especially for a large featured dataset. This paper focuses on an alternative approach where stochastic automata can be learned from time-series observations captured from a set of deployed sensors. The main advantage offered by such techniques is that they enable analysis and forecasting from a formal model instead of traditional learning methods. We perform statistical model checking to analyze the learned automata by expressing temporal properties. For this purpose, we consider a critical water infrastructure that provides a scenario based on a set of input and output values of heterogeneous sensors to regulate the dam spill gates. The method derives a consistent approximate model with traces collected over thirty years. The experiments show that the model provides not only an approximation of the desired output of a feature value but, also, forecasts the ebb and flow of the sensed data.

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Notes

  1. Sistema Integral del Ciclo del Agua (Integral System of Water Cycle)

  2. http://www-verimag.imag.fr/TOOLS/DCS/bip/doc/latest/html/index.html

  3. http://www-verimag.imag.fr/BIP-SMC-A-Statistical-Model-Checking.html?lang=en

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Acknowledgements

The authors would like to thank EMALCSA Company for the data collected from the dam infrastructure.

Funding

The research leading to the presented results has been undertaken within the research profile Brain-IoT - model-Based fRamework for dependable sensing and Actuation in INtelligent decentralized IoT systems, funded by the European Union, grant number: 780089.

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Authors and Affiliations

  1. VERIMAG, Université Grenoble Alpes, Grenoble, France

    Abdelhakim Baouya, Salim Chehida & Saddek Bensalem

  2. École d’Ingénieur CESI, Aix-en-Provence, France

    Samir Ouchani

  3. CNRS, VERIMAG, Université Grenoble Alpes, Grenoble, France

    Marius Bozga

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  1. Abdelhakim Baouya
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Baouya, A., Chehida, S., Ouchani, S. et al. Generation and verification of learned stochastic automata using k-NN and statistical model checking. Appl Intell 52, 8874–8894 (2022). https://doi.org/10.1007/s10489-021-02884-4

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