Signal temporal logic for mining guard conditions in hybrid system models
Publication date
2026-05-07
Document type
Konferenzbeitrag
Author
Engeln, Ulrike
Schmidt, Maximilian
Plambeck, Swantje
Fey, Görschwin
Schupp, Sybille
Organisational unit
Hamburg University of Technology
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Machine learning for cyber physical systems : proceedings of the conference ML4CPS 2026
First page
91
Last page
100
Peer-reviewed
✅
Part of the university bibliography
Nein
Language
English
Keyword
Specification mining
Signal temporal logic
Hybrid systems
Cyber-physical systems
Abstract
Hybrid systems are used to model cyber-physical systems. They combine continuous dynamics with discrete switching behavior. The manual identification of hybrid system models is time-consuming and error-prone, motivating data-driven approaches to hybrid system identification. Existing approaches focus primarily on learning the continuous dynamics and discrete modes of a hybrid automaton. For the guard conditions between modes, they apply probabilistic methods, which lack interpretable and verifiable representations. We propose the usage of signal temporal logic (STL) for learning guards. The advantages of our approach are twofold: it introduces human-readable and verifiable descriptions of transition logic and allows integrating prior knowledge by specifying templates for expected guard structures. Empirical results show that the accuracy of our approach is comparable to existing decision tree-based methods with respect to reconstructing system behavior. The learned guards are more expressive and interpretable. By this, we advance hybrid system identification towards trustworthy and explainable data-driven modeling.
Description
This contribution is part of the conference proceedings, which are licensed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)
Version
Published version
Access right on openHSU
Open access