Dr. Rolf Würtz
Institut für Neuroinformatik
Ruhr-Universität Bochum
D-44780 Bochum, Germany
http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/rolf
The tutorial will start with a discussion of information technology (IT) problems caused by the rapidly increasing complexity of systems to be deployed. In the world of living beings on the other hand, it can be observed that extremely complex systems function in a robust, fault-tolerant, flexible, adaptive, self-organizing way, and apparently goal-directed way.
It is therefore intriguing to identify strategies by means of which these properties are achieved by living systems. This ``Learning from Nature'' is the founding idea of Organic Computing. Earlier IT applications include Neural Networks and Evolutionary Computation. The current interest in Organic Computing is also sustained by a notion of ``Organic'' which relates to the user rather than the developer. In that aspect, Organic Computing requires that the interface between the IT system and the user be organic, intelligible, and friendly. This again imposes constraints on the user interfaces, which can only be partly fulfilled by current technology. The introduction is followed by some facts and theories about self-organizing systems including a short description of current research projects and open issues. One project develops flexible control of traffic for the city of Hanover, which tries to optimizes the overall flow without relying on centralized controllers. Within the automobile, the exploding number of components and interactions and the combinatorics of possible models has prompted the development of an evolutionary architecture which self-organizes according to a goal description and reorganizes in the presence of partial failure. In an ongoing project, the organization of varying office users and people looking for them within the building is handed over to an organic system.
The application domain I will present in detail is computer vision and user interaction. First a system for automatic face recognition is described which has been constructed according to neurobiological findings and a theory of self-organizing neural networks. It is also an example for the hierarchical self-organization of elementary feature detectors into structures of higher and higher complexity. Detailed self-organizing neuronal dynamics are presented as well as the techniques of pyramid matching and Elastic Graph Matching, the latter being more efficient on digital computers. The basic matching mechanism is extended to the bunch graph data format and recognition procedure, which has made this technology one of the leading methods for facial identification. This data format can be used to learn facial attributes like ``gender,'' ``beardedness,'' ``wearing glasses'' solely from examples, without need for formulation of rules defining these attributes. In a currently ongoing project, it is used to diagnose certain genetic diseases from facial images. The extension from faces to body gestures is more complicated and partly subject of ongoing research. I present the method of ``democratic integration'', which allows for flexible integration of many fragile cues into a robust decision. This is used for user interaction with a robot gripsee and for interpretation of user gestures. Concluding the tutorial I present a system for the self-organization of a recognition memory for everyday objects. Again the focus is on automatic learning from examples.
Rolf P. Würtz obtained his diploma in Mathematics from the University of Heidelberg, Germany in 1986. After that, he was research assistant
at the Max-Planck-Institute for Brain Research in Frankfurt, Germany.
In 1990, he joined the Institute for Neurocomputing at the University
of Bochum, Germany, where he received his Ph.D. from the Physics department in 1994. Until 1997, he was a postdoctoral researcher at
the department of Computing Science at the University of Groningen,
The Netherlands. He is currently a scientific staff member and
lecturer at the Institute for Neurocomputing in Bochum and has
lectured at the universities of Barcelona, Ilmenau, and
Helsinki. Since August 2006 he also acts as stand-in chairholder at
the Systems Biophysics chair at the Institute for Neurocomputing in
Bochum.
Research interests include neuronal models and efficient algorithms for face and object recognition, hand-eye coordination, integration of visual and tactile information, video analysis, and links to higher cognition. General organization problems and solution methods inspired by living systems are studied under the framework of "Organic Computing". He is associate editor of "Neural Networks", senior member of IEEE, and has published 65 papers in journals and peer-reviewed books. He is currently editing a book on "Organic Computing".