Maximizing information in the sensory-motor loop

Cognitive systems are embodied and situated, which means that they act and learn within the sensori-motor loop. An open question is how such systems can improve their behaviour through self-organised learning. In this talk, we present an approach in which such a learning rule is derived from the principle of maximising an approximation of the predictive information. First experiments already show that such a principle enables the co-ordinated behaviour of physically coupled robots.