Categorization, representations, and the dynamics of system-environment interaction: a case study in autonomous systems

Since the world is partly an unpredictable place the agents that have to function in it have to rely on learning to adjust to it. To understand the adaptive properties of autonomous agents, that are related to their learning capacities, it is necessary to explore what they exactly learn. In order to do this we will further analyse an autonomous agent designed according to the methodology of distributed adaptive control. It is shown by means of a simulation study that the mappings between sensing and acting the system acquires through its interaction with the environment are topology preserving. Moreover, on the basis of these results it is shown that these mappings implement action related prototypes. This is demonstrated by translating the mapping back into world coordinates. Based on these results an extension of the model is proposed that illustrates another aspect of our methodology; stretching a model. To overcome some limitations of Hebbian learning, which is crucial for the self-organizing properties of the model, an expectancy mechanism is included in the control architecture. This allows the development of mechanisms that influence the categorization process independently of immediate sensory states. This can be seen as a necessary next step to come to a closer definition of the concept of representation in the context of autonomous agents.