Cognitive Behavior of Humans, Animals, and Machines:

Situation Model Perspectives

Oktober 2019 – Juli 2020

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

Malte Schilling


Foto Neuroinformatics, Faculty of Technology
& Center for Cognitive Interaction Technology (CITEC),
Bielefeld University, Germany
E-Mail: mschilli@techfak.uni-bielefeld.de


Malte Schilling is a Responsible Investigator at the Center of Excellence for 'Cognitive Interaction Technology' in Bielefeld. His work concentrates on internal models, their grounding in behavior and application in higher-level cognitive function like planning ahead or communication. Before, he was a PostDoc at the ICSI in Berkeley and did research on the connection of linguistic to sensorimotor representation. He received his PhD in Biology from Bielefeld University in 2010 working on decentralized biologically-inspired minimal cognitive systems. He has studied Computer Science at Bielefeld University and finished 2003 the Diploma with his thesis on knowledge-based systems for virtual environments.

Current Main Research Interests

In general, I am interested in how adaptive behavior is controlled. One key insight taken from biology is a hierarchical and modular organization of motor control structures that helps to address motor control problems. Currently, I am transferring such a modular and highly parallelized structure into the framework of deep reinforcement learning. The goal is to understand how adaptivity can emerge, in particular on different time scales and spanning different levels of a control hierarchy. Furthermore, I am interested in neural representation and how these can be grounded in lower level behaviors. One early such representation is the notion of an internal model of the own body and I am currently investigating how a population-based encoded model can be recruited for motor control or observing somebody else's actions.

Five selected publications with particular relevance to the Research Group
  • Schilling, M., Ritter, H., & Ohl, F. W. (submitted). From crystallized adaptivity to fluid adaptivity in deep reinforcement learning — Insights from biological systems on adaptive flexibility. Submitted to IEEE Systems, Man, and Cybernetics 2019 Bari, Italy.
  • Dürr, V., & Schilling, M. (2018). Transfer of spatial contact information among limbs and the notion of peripersonal space in insects. Frontiers in Computational Neuroscience, 12, 101. doi:10.3389/fncom.2018.00101
  • Schilling, M., & Melnik, A. (2018). An approach to hierarchical deep reinforcement learning for a decentralized walking control architecture. In A. Samsonovich (Eds.) Biologically Inspired Cognitive Architectures 2018. BICA 2018. Advances in Intelligent Systems and Computing, 848, 272-282. Springer, Cham.
  • Schilling, M., & Cruse, H. (2017). ReaCog, a minimal cognitive controller based on recruitment of reactive systems. Frontiers in Neurorobotics, 11, 3. doi:10.3389/fnbot.2017.00003
  • Baum, M., Meier, M., & Schilling, M. (2015). Population based mean of multiple computations networks: A building block for kinematic models. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 1-8.