First published 20 July 2015
Quantifying Self-Organizing Behavior of Autonomous Robots
Georg Martius and Eckehard Olbrich
In recent years research in autonomous robots has been more and more successful in developing algorithms for generating behavior from a generic task-independent objective. Examples are intrinsic motivations for artificial curiosity, empowerment, homeokinesis, and maximizing predictive information. Independently of its origin, it would be useful to quantify behavior, in order to objectively compare algorithms with each other or even with human or animal movements. We investigate different methods for extracting characteristic measures based on information theoretic quantities.