Multi-class Temporal Logic Neural Networks
Danyang Li Roberto Tron
Time-series data can represent the behaviors of autonomous systems, such as
drones and self-driving cars. The problem of binary and multi-class
classification has received a lot of attention in this field. Neural networks
represent a popular approach to classifying data; However, they lack
interpretability, which poses a significant challenge in extracting meaningful
information from them. Signal Temporal Logic (STL) is a formalism to describe
the properties of timed behaviors. We propose a method that combines all of the
above: neural networks that represent STL specifications for multi-class
classification of time-series data. We offer two key contributions: 1) We
introduce a notion of margin for multi-class classification, and 2) we
introduce the use of STL-based attributes for enhancing the interpretability of
the results. We evaluate our method on two datasets and compare with
state-of-the-art baselines.