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arXiv · 1711.06202

A Robust Genetic Algorithm for Learning Temporal Specifications from Data

Laura Nenzi·Simone Silvetti·Ezio Bartocci·Luca Bortolussi
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Abstract

We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system. We assume the training set to be labeled by human experts and that we have access only to a limited amount of data, typically noisy. We provide a systematic approach to synthesize both the syntactical structure and the parameters of the temporal logic formula using a two-steps procedure: first, we leverage a novel evolutionary algorithm for learning the structure of the formula; second, we perform the parameter synthesis operating on the statistical emulation of the average robustness for a candidate formula w.r.t. its parameters. We compare our results with our previous work [{BufoBSBLB14] and with a recently proposed decision-tree [bombara_decision_2016] based method. We present experimental results on two case studies: an anomalous trajectory detection problem of a naval surveillance system and the characterization of an Ineffective Respiratory effort, showing the usefulness of our work.

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Laura Nenzi, Simone Silvetti, Ezio Bartocci, Luca Bortolussi. 2017-11-13. A Robust Genetic Algorithm for Learning Temporal Specifications from Data. https://arxiv.org/abs/1711.06202

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