Time-Incremental Learning from Data Using Temporal Logics

TL;DR

This paper presents a novel time-incremental learning framework that uses Signal Temporal Logic specifications and neural networks to predict labels of prefix signals in cyber-physical systems, enabling real-time decision-making.

Contribution

It introduces a new decision-tree based method to generate STL specifications and a neural network approach to learn time-variant weights for signal classification.

Findings

Effective in urban-driving case study

Achieves high classification accuracy

Enables real-time, human-interpretable predictions

Abstract

Real-time and human-interpretable decision-making in cyber-physical systems is a significant but challenging task, which usually requires predictions of possible future events from limited data. In this paper, we introduce a time-incremental learning framework: given a dataset of labeled signal traces with a common time horizon, we propose a method to predict the label of a signal that is received incrementally over time, referred to as prefix signal. Prefix signals are the signals that are being observed as they are generated, and their time length is shorter than the common horizon of signals. We present a novel decision-tree based approach to generate a finite number of Signal Temporal Logic (STL) specifications from the given dataset, and construct a predictor based on them. Each STL specification, as a binary classifier of time-series data, captures the temporal properties of the dataset over time. The predictor is constructed by assigning time-variant weights to the STL formulas. The weights are learned by using neural networks, with the goal of minimizing the misclassification rate for the prefix signals defined over the given dataset. The learned predictor is used to predict the label of a prefix signal, by computing the weighted sum of the robustness of the prefix signal with respect to each STL formula. The effectiveness and classification performance of our algorithm are evaluated on an urban-driving and a naval-surveillance case studies.