The institutes’s research and teaching activities are concerned with fundamental methods for automated and data-based knowledge extraction. The institute pursues a holistic approach in which central aspects of intelligent systems are researched in a unified manner. In particular, concepts and methods from machine learning, learning in deep neural networks, reinforcement learning, and embodied intelligence are integrated. Mathematical theory development plays a central role here and is supported and guided by experimental work in a robotics lab.
Of particular interest is the data-driven extraction of causal, as opposed to merely associational, knowledge. Decision making, action, and behaviour rely on this kind of knowledge and the coherent coupling of its various representational levels, ranging from the sub-symbolic to the symbolic level of representation.
Methodologically, the institute has a strong focus on information theory and geometry, both integrated within the field of information geometry.
