A kernel function for Signal Temporal Logic formulae
This work addresses a specific challenge in formal methods and machine learning integration, but appears incremental as it applies existing kernel methods to a new domain.
The authors tackled the problem of defining a kernel function for Signal Temporal Logic (STL) formulae, enabling embedding into a Hilbert space and facilitating kernel-based machine learning applications, with a demonstration on a regression problem for probabilistic models.
We discuss how to define a kernel for Signal Temporal Logic (STL) formulae. Such a kernel allows us to embed the space of formulae into a Hilbert space, and opens up the use of kernel-based machine learning algorithms in the context of STL. We show an application of this idea to a regression problem in formula space for probabilistic models.