KI2025 - 48th German Conference on Artificial Intelligence

KI2025 is the 48^th German Conference on Artificial Intelligence organized in cooperation with the Fachbereich Künstliche Intelligenz der Gesellschaft für Informatik (GI-SIG AI). The German AI Conference basically started approx. 50 years ago with the first GI-SIG AI meeting on Oct. 7, 1975 (see the archive of GI-SIG AI).

KI is one of the major European AI conferences and traditionally brings together academic and industrial researchers from all areas of AI, providing an ideal place for exchanging news and research results on theory and applications. KI2025 is organized in combination with INFORMATIK 2025.

Barbara Hammer is a full Professor for Machine Learning at the CITEC Cluster at Bielefeld University, Germany. She received her Ph.D. in Computer Science in 1999 and her venia legendi (permission to teach) in 2003, both from the University of Osnabrueck, Germany, where she chaired a junior research group on 'Learning with Neural Methods on Structured Data'. In 2004, she became Professor for Theoretical Computer Science at Clausthal University of Technology, Germany, before moving to Bielefeld as chair of the Machine Learning group in 2010. Several research stays took her to University Paris I, University of Padova, Birmingham University, and CAIR Bangalore.

Barbara's research interests cover theory and algorithms in machine learning and neural networks and their application for technical systems and the life sciences, including explainability, learning with drift, nonlinear dimensionality reduction, recursive models, and learning with non-standard data.

When engaging in social interaction, people rely on their ability to reason about other people’s mental states, including goals, intentions, and beliefs. This theory of mind ability allows them to more easily understand, predict, and even manipulate the behavior of others. People can also use their theory of mind recursively, to reason about the theory of mind of others, that is, to apply higher-order theory of mind, which allows them to understand sentences like “Alice believes that Bob does not know that she wrote a novel under pseudonym”.

In the current era of hybrid intelligence, teams may consist of humans, robots and software agents. For better coordination, it would be beneficial if the computational members of the team could recursively reason about the minds of their human colleagues. While the usefulness of higher orders of theory of mind is apparent in many social interactions, empirical evidence so far suggests that people usually do not use this recursive ability spontaneously, even when doing so would be highly beneficial. In this lecture, we discuss some of our computational modelling research and empirical experiments. How do children develop second-order theory of mind? How do they learn to catch a liar? Can we entice adults to engage in higher-order theory of mind reasoning by letting them negotiate with computational agents? What’s logic got to do with reasoning about reasoning about reasoning? Do corvids have any theory of mind? And how about ChatGPT?

Rineke Verbrugge is a pioneer in building bridges between logic and cognitive science. Specifically, she was the first one to combine dynamic epistemic logic and computational cognitive modeling to study theory of mind. Verbrugge is a full professor, holding the chair of Logic and Cognition at the University of Groningen’s Institute of Artificial Intelligence, ALICE. Since 2002, she has been the leader of the Multi-agent Systems research group in Groningen, first as an associate professor and since 2009 as a full professor.