Organization of Information Flows in the Perception-Action Loop of Agents

Information is an essential and omnipresent resource and has long been suspected as a major factor shaping the emergence of intelligence in animals and as a guideline to construct artificial intelligent systems. In search for fundamental principles guiding the self-organization of neural networks, Linsker (1988) formulated a number of information-theoretic hypotheses. His model (and most of its successors) was purely passive. However, recent work by Touchette and Lloyd (2000) extending early work by Ashby (1953), as well as some work by Polani et al. (2001) has shown that actions can be incorporated into the information- theoretical analysis.

As was found by Klyubin et al. (2004), incorporating actions into an information-theoretic formalization of the perception action-loop of agents has dramatic consequences in terms of self-organization capabilities of the processing system. As opposed to Linsker's model which required some significant pre-structuring of its neural network, this new model makes only minimal assumptions about the information processing architecture. The agent's "embodiment", i.e. the coupling of its sensors and actuators to the environment, is sufficient to give rise to structured pattern detectors driven by optimization principles applied to the information flow in the system.

In the present talk, we will motivate Shannon information as a primary resource of information processing, introduce a model which allows to consider agents purely in terms of information and show how this model gives rise to the aforementioned observations. If there is time, the talk will discuss the use of information-theoretic methods to structure the information processing also in real robot systems.