We integrate human and AI knowledge in systems

Knowledge-based AI approaches are characterized by the fact that knowledge (e.g. expert knowledge) can be stored, generated, used and queried explicitly and in a comprehensible way. Machine learning approaches, on the other hand, implicitly derive relationships "on the fly" from the data presented to them. They usually require significant computer capacities and massive amounts of high-quality and sufficiently task-specific data. Hybrid AI approaches that combine knowledge-based and data-driven AI contribute to the improved applicability of machine learning processes and, last but not least, to their interpretability.

On this basis, we develop IT solutions that make it possible to provide human operators in networked systems with relevant information and knowledge at the right time, at the right level and according to their specific task. Essential for this are not only suitable procedures for information acquisition and exploitation, but also adequately designed human-machine interfaces, in particular for the realization of interactive assistance systems.