Research Area C

Knowledge & Learning

The ultimate goal of the CoTeSys cluster is the realization of technical systems which "know what they are doing", which can "assess how well they are doing", and "improve themselves based on this knowledge".


To this end, research area C will design and develop a computational model for knowledge processing and learning especially designed to be implemented on computing platforms which are embedded into sensor-equipped technical systems acting in physical environments.

This model -- implemented as a knowledge processing and learning infrastructure -- will enable technical systems to learn new skills and activities from potentially very little experience, in order to optimize and adapt their operations, to explain their activities and accept advice in joint human-robot action, to have meta knowledge of their own capabilities and behavior,and to respond to new situations in a robust way.

The research topics that define CoTeSys' unique approach to knowledge and learning in CTSs and where CoTeSys plans drive the state-of-the-art include first, the development of probabilistic framework as a means for combining first-order representations with probability or uncertainty in order to provide a common foundation for integrating perception, learning, reasoning, and action, while accommodating uncertainty. Second, a computational model of "ActionMeta-Knowledge", which considers actions as information processing units that automatically learn and maintain various models of themselves and the behavior they generate and use the models for behavior tuning, skill learning, failure recovery, self-explanation, and diagnosis. Third, a comprehensive repertoire of sequence learning methods, partly based on theories of optimal learning algorithms. Finally, an embedded integrated learning architecture employing multiple and diverse learning mechanisms capable of generalizing from very little experience.