Prof. Dr. Martin V. Butz
University of Tübingen
Computer Science, Cognitive Modeling
Sand 14, 72076 Tübingen Germany
Phone: +49 (0)7071 29 70429
Sprechstunde: Mi 10 - 12 (während des Semesters)
Since the 1st of September 2011 my team and I transferred from the Department of Cognitive Psychology, University of Würzburg to Tübingen, where I am now a full professor in Cognitive Modeling.
My team is developing cognitive bodyspaces, that is, interactive spatial representations of the body (or rather simulated body-like structures) within its environment, investigating methods of learning such representations, shaping them maximally behaviorally suitable, and actually triggering flexible, adaptive, self-motivated goal-directed behavior within.Moreover, more recently we are also investigating how more abstract, conceptual representations can develop out of these spatial representations and the behavioral control that is realized mediated by these representations.
Further Research Interests
My major research interest lies in the analysis and development of anticipatory cognitive systems, that is, systems that self-develop suitable sensorimotor structures in order to efficiently act goal-directed. Recent research insights, spanning from cognitive psychology, neuroscience, and linguistics to artificial intelligence, suggest that anticipations can be found in a large variety of cognitive mechanisms. It appears that anticipations (in a broad sense) form the basis for effective adaptive behavior as well as (life-long) learning in general. Recently, I postulated that an anticipatory drive underlies, directs, and shapes our emerging inner realities and self perceptions, essentially forming the structural foundations for self-consciousness. This work was published as a target article, received attention from renowned researchers in various disciplines, and triggered a pulsating discussion (Butz, 2008).
The workshop series on Anticipatory Behavior in Adaptive Learning Systems (ABiALS) is designed to investigate anticipatory mechanisms and structures in detail, analyzing different types of predictive and anticipatory systems and propagating their development. The accompanying books (2003, 2007, & 2009) provide comprehensive overviews, including philosophical considerations, conceptualizations, as well as concrete system implementations and evaluations. Complementing the study of anticipatory behavior, I am now focusing also on the design and analysis of self-developing systems. These systems develop pro-active spatial representations, that is, bodyspaces and integrated object-interaction structures that are shaped to be maximally suitable for effective, goal-directed decision making and interaction.
Moreover, my research on "Rule-based Evolutionary Online Learning Systems: A Principal Approach to LCS Analysis and Design" (Butz, 2006) investigates how to combine gradient-based and evolutionary-based techniques to optimize spatial clustering for highly general but accurate predictions. By now, we have shown that the investigated XCS system is a highly interactive learning system that can solve a large variety of typical cognitive and adaptive learning problems. Currently, we are expanding the capabilities of XCSF to learn locally linear sensorimotor forward-inverse models and compare to the similar LWPR system.
The last ABiALS workshop was organized in 2011. Further information can be found on the ABiALS webpage.
Further information on the offered courses can be found on the dedicated webpages.