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.
Engeln et al. (Thu,) studied this question.