The group is involved in the following projects:

LeaRNNify

The DAAD-supported project LeaRNNify is at the interface of formal methods and artificial intelligence. Its aim is to bring together two different kinds of algorithmic learning, namely grammatical inference and learning of neural networks.

More precisely, we promote the use of recurrent neural networks (RNNs) in the process of verifying reactive systems, which until now has been reserved for grammatical inference. On the other hand, grammatical inference is finding its way into the field of classical machine learning. In fact, our second goal is to use automata-learning techniques to enhance the verification, explainability, and interpretability of machine-learning algorithms and, in particular, RNNs.

Also visit our project website.

Temporal Logic Sketching

The goal of the DFG-funded project Temporal Logic Sketching is to develop computer-aided methods to assist engineers in writing formal specifications. To this end, the project combines inductive and deductive techniques from the areas of machine learning, artificial intelligence, and logic. As a byproduct, the project also investigates explainable machine learning, specifically learning of human-interpretable models.