Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems

• URL: http://arxiv.org/abs/2011.00384v3
• Date: Sat, 24 Jul 2021 19:24:09 GMT
• Title: Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems
• Authors: Meiyi Ma, John Stankovic, Ezio Bartocci, Lu Feng
• Abstract summary: We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) We propose a new logic named emphSignal Temporal Logic with Uncertainty (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences. We evaluate the proposed approach via experiments with real-world datasets and a simulated smart city case study.
• Score: 2.3131309703965135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predictive monitoring -- making predictions about future states and monitoring if the predicted states satisfy requirements -- offers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets. We propose a new logic named \emph{Signal Temporal Logic with Uncertainty} (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences predicted by Bayesian RNNs. We define STL-U strong and weak satisfaction semantics based on if all or some sequences contained in a flowpipe satisfy the requirement. We also develop methods to compute the range of confidence levels under which a flowpipe is guaranteed to strongly (weakly) satisfy an STL-U formula. Furthermore, we develop novel criteria that leverage STL-U monitoring results to calibrate the uncertainty estimation in Bayesian RNNs. Finally, we evaluate the proposed approach via experiments with real-world datasets and a simulated smart city case study, which show very encouraging results of STL-U based predictive monitoring approach outperforming baselines.

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