A Robust Genetic Algorithm for Learning Temporal Specifications from Data
This work addresses the challenge of learning temporal specifications from noisy, limited data for applications like surveillance and medical monitoring, but it is incremental as it builds on prior evolutionary and decision-tree approaches.
The authors tackled the problem of mining signal temporal logic formulas from labeled trajectory data to distinguish between regular and anomalous behaviors, achieving results comparable to existing methods in two case studies.
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.