Multi-class Temporal Logic Neural Networks

• URL: http://arxiv.org/abs/2402.12397v2
• Date: Tue, 25 Jun 2024 02:58:06 GMT
• Title: Multi-class Temporal Logic Neural Networks
• Authors: Danyang Li, Roberto Tron,
• Abstract summary: Time-series data can represent the behaviors of autonomous systems, such as drones and self-driving cars. We propose a method that combines neural networks that represent STL specifications for multi-class classification of time-series data. We evaluate our method on two datasets and compare it with state-of-the-art baselines.
• Score: 8.20828081284034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time-series data can represent the behaviors of autonomous systems, such as drones and self-driving cars. The task of binary and multi-class classification for time-series data has become a prominent area of research. 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 that describes 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 STL-based attributes for enhancing the interpretability of the results. We evaluate our method on two datasets and compare it with state-of-the-art baselines.

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