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.

本文提供了一种系统的方法来合成时序逻辑公式的语法结构和参数，其中利用了一种新颖的进化算法来学习公式的结构，然后对候选公式的平均鲁棒性执行参数综合，以应对动力系统的异常轨迹检测和无效呼吸努力的表征两个案例研究。