ZiF Research Group

Cognitive Behavior of Humans, Animals, and Machines:

Situation Model Perspectives

October 2019 – July 2020

Convenors: Werner Schneider (Bielefeld, GER), Helge Ritter (Bielefeld, GER)

Kerstin Schill

Associate Fellow

Foto Faculty of Computer Science and Mathematics,
University of Bremen, Germany
E-Mail: kschill@uni-bremen.de
Homepage

CV

Kerstin Schill studied Informatics at the Technical University Munich and received her PhD in Human Biology from the Ludwig-Maximilian-University Munich (LMU) in 1993. From 1993 to 2003, she worked as a post doc and head of the research group "Computational Intelligence" at the Institute for Medical Psychology at the LMU. In 2003, Kerstin Schill was appointed Full Professor of Computer Science at the University of Bremen. Since 2012, she is also the Dean of the faculty of Computer Science and Mathematics at the University of Bremen and since 2018, she is the Rector of the Hanse-Wissenschaftskolleg (Institute for Advanced Studies). Moreover, Kerstin Schill is Senator of the Deutsche Forschungsgemeinschaft (German Science Foundation) since 2004 and co-speaker and member of the Board of the CRC EASE since 2017.

Current Main Research Interests

In the course of evolution biological systems have developed cognitive and intelligent abilities which are still more efficient and powerful than today's technical systems. Seeing, hearing, tactile perception as well as localisation in and exploration of spatial environments are some examples of such cognitive abilities. The process of learning and subsequent decision-making are examples of more human-like cognitive capacities. First, we try to understand and formalize these abilities, such that we can map them to theoretical approaches. Finally, we seek to transfer them into intelligent technical systems. The resultant systems combine low level cognitive abilities like pattern recognition with higher level cognitive capacities like reasoning and planning. Our research includes developments in theories of automated learning, processing and representation of uncertain knowledge. Further research interests are the development of decision processes, which are based on the principle of information gain. In addition, we investigate and model the fusion of multisensory information, numerosity, natural scene statistics, SLAM (simultaneous localisation and mapping), attentional processes, and deep learning.

Five selected publications with particular relevance to the Research Group
  • Clemens, J., & Schill, K. (2016). Extended Kalman filter with manifold state representation for navigating a maneuverable melting probe. 19th International Conference on Information Fusion (FUSION), 1789-1796.
  • Nakath, D., Rachuy, C., Clemens, J., & Schill, K. (2016). Optimal rotation sequences for active perception. Proceedings of SPIE Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2016, 9872, 987204. SPIE Press.
  • Reineking, T., & Schill, K. (2014). Evidential object recognition based on information gain maximization. In F. Cuzzolin (Ed.), International Conference on Belief Functions , 8764, 227-236. Springer, Cham.
    best paper award
  • Zetzsche, C., Gerkensmeyer, T., Schmid, F., & Schill, K. (2012). Sensorimotor Representation of Space: Application in Autonomous Systems and in a Wayfinding Assistant for Alzheimer's Disease. Proceedings of the IEEE/ACIS 11th International Conference on Computer and Information Science, 219-224.
  • Schill, K., Zetzsche, C., & Hois, J. (2009). A belief-based architecture for scene analysis: From sensorimotor features to knowledge and ontology. Fuzzy Sets and Systems, 160, 1507-1516.