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L. Nenzi, S. Silvetti, E. Bartocci, L. Bortolussi:
"A Robust Genetic Algorithm for Learning Temporal Specifications from Data";
Vortrag: Proc. of QEST 2018: the 15th International Conference on Quantitative Evaluation of Systems, Beijing, China; 04.09.2018 - 07.09.2018; in: "Proc. of QEST 2018: the 15th International Conference on Quantitative Evaluation of Systems", 11024 (2018), S. 323 - 338.

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 [9] and with a recently proposed decision-tree [8] 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.

http://dx.doi.org/10.1007/978-3-319-99154-2