A Robust Genetic Algorithm for Learning Temporal Specifications from Data

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 test our algorithm on a anomalous trajectory detection problem of a naval surveillance system and we compare our results with our previous work [1] and with a recently proposed decision-tree [2] based method. Our experiments indicate that the proposed approach outperforms our previous work w.r.t. accuracy and show that it produces in general smaller and more compact temporal logic specifications w.r.t. the decisiontree based approach with a comparable speed and accuracy.