Intelligent systems behave in ways that we associate with intelligence. Yet intelligence is multifaceted - it can refer to individuals, but also to entire populations and their development. Intelligent systems can interact with their environment, taking into account natural senses and actuators. In doing so, their capabilities are often superior even to natural systems, such as in processing visual, auditory, haptic, or olfactory information.
Exemplary research topics include the following:
- Modeling physical properties of real environments to create virtual images of reality.
- Visualization and analysis of data and processes in engineering, bio- and natural sciences
- Support for knowledge discovery ("data mining") in extremely large data sets using machine learning
- Modality of interaction between users and the environment, e.g., in "smart spaces" using sensors and pattern recognition methods
- User interaction behavior and adoption of intelligent systems in the operational environment
- Lukas-Valentin Herm, Kai Heinrich, Jonas Wanner, Christian Janiesch: Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability. Int. J. Inf. Manag. 69: 102538 (2023). DOI: 10.1016/j.ijinfomgt.2022.102538
- Hans-Christian Möhring, Petra Wiederkehr, Kaan Erkorkmaz, Yasuhiro Kakinuma: Self-optimizing machining systems. CIRP Annals 69(2):740-763 (2020). DOI: 10.1016/j.cirp.2020.05.007
- Jan Robine, Marc Höftmann, Tobias Uelwer, Stefan Harmeling: Transformer-based World Models Are Happy With 100k Interactions. ICLR 2023. DOI: 10.48550/arXiv.2303.07109
- Erich Schubert, Peter J. Rousseeuw: Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms. Inf. Syst. 101: 101804 (2021). DOI: 10.1016/j.is.2021.101804
- Stephan Wenninger, Jascha Achenbach, Andrea Bartl, Marc Erich Latoschik, Mario Botsch: Realistic Virtual Humans from Smartphone Videos. VRST 2020: 29:1-29:11. DOI: 10.1145/3385956.3418940
- Xuan Xie, Kristian Kersting, Daniel Neider: Neuro-Symbolic Verification of Deep Neural Networks. IJCAI 2022: 3622-3628. DOI: 10.24963/ijcai.2022/503