Applied Artificial Intelligence

Contact

Group Lead

Prof. Dr.-Ing. Daniel Sonntag

Office

Office hours by appointment

Address

Stiftungsprofessur Künstliche Intelligenz
Marie-Curie Str. 1
D-26129 Oldenburg

See also

Applied Artificial Intelligence

The “Applied AI” research group, which is part of the Interactive Machine Learning research department at the German Research Center for Artificial Intelligence (DFKI), focuses on applying and adapting artificial intelligence methods to, for example, industrial and medical applications. Sustainability is also a major topic in Oldenburg.

Research-relevant application aspects primarily concern the use of learning systems and intelligent user interfaces. Key areas of focus include multimodal input and output and multisensor applications involving environmental and state recognition, sensor data processing, and issues of real-time performance and interactivity when learning from very large or very small datasets, as well as reliability aspects (including trust in AI and explainable AI).

Regardless of specific subject areas, the overarching research goal is to develop guidelines for the practical application of artificial intelligence. In addition, basic research is conducted in the interdisciplinary field of human–machine interaction in combination with machine learning.

Student AI transfer projects are especially important to us. You can find a selection here: iml.dfki.de. For Bachelor's or Master's theses, please contact or .

News

Best Paper Award at CHI 2026

Major success for AAI member Christoph A. Johns: Together with Julien Gori (Sorbonne University), Aurelien Nioche (University of Glasgow), and Antti Oulasvirta (Aalto University), he received a Best Paper Award at the prestigious CHI 2026 for the paper “A decision-theoretic representation of assistive interfaces.”

At the ACM CHI Conference on Human Factors in Computing Systems (CHI 2026)—the world’s leading conference in the field of human-computer interaction—Julien Gori (Sorbonne University), Aurelien Nioche (University of Glasgow), Christoph A. Johns (University of Oldenburg), and Antti Oulasvirta (Aalto University) were honored with the prestigious Best Paper Award for being among the top 1% of accepted research papers. Their paper, “A decision-theoretic representation of assistive interfaces,” addresses a fundamental gap in the development of assistive systems.

While adaptive interfaces, recommendation services, and intelligent assistants are ubiquitous in computer science, there has been a lack of a common conceptual foundation across different disciplines. In their paper, the authors present a formal model that describes assistance as a sequential decision-making process under uncertainty between two agents—the user and the assistant.

The research, which was also conducted as part of AAI postdoc Christoph A. Johns’ dissertation at Aarhus University, uses so-called Partially Observable Stochastic Games (POSGs) to mathematically capture interaction dynamics. For the first time, the model allows concepts such as adaptation, augmentation, and delegation to be mathematically defined and treats assistance as an optimization problem. In addition to the theoretical derivation, the researchers presented an implementation in the form of a Python library that enables the practical use of this framework.

Paper in ACM Digital Library: https://doi.org/10.1145/3772318.3791819

(Changed: 11 Feb 2026)  Kurz-URL:Shortlink: https://uol.de/p79699n13267en
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