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Article Dans Une Revue Applied Intelligence Année : 2022

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

Résumé

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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Dates et versions

hal-04094178 , version 1 (14-05-2024)

Identifiants

Citer

Abdelhakim Baouya, Salim Chehida, Samir Ouchani, Saddek Bensalem, Marius Bozga. Generation and verification of learned stochastic automata using k-NN and statistical model checking. Applied Intelligence, 2022, 52 (8), pp.8874-8894. ⟨10.1007/s10489-021-02884-4⟩. ⟨hal-04094178⟩
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