A Robust Genetic Algorithm for Learning Temporal Specifications from Data

TL;DR

This paper introduces a robust genetic algorithm that learns temporal logic specifications from limited, noisy data, enabling effective anomaly detection and system characterization.

Contribution

It presents a novel evolutionary algorithm for jointly learning the structure and parameters of temporal logic formulas from data.

Findings

Successfully applied to naval surveillance anomaly detection

Effective in characterizing respiratory effort issues

Outperforms previous methods in case studies

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.