Game analysis and control by means of continuously learning networks

The paper deals with the question, if and how the process of learning can be modelled, analysed and maybe improved by means of Neural Networks. The problem is that most of the developed types of network are designed for restricted technical purposes only. The similarity to neural brain structure is reduced to some basic formulae describing algorithms of static learning in the meaning of pattern recognition and activity selection.

Using a more dynamic approach, however, it seems to be possible to model dynamic aspects of learning and decision processes as well. One step in this direction has been done with the Dynamically Controlled Network (DyCoN), which - in order to support continuous learning - has been developed on the basis of a conventional Kohonen Feature Map (KFM) and has been tested successfully in different areas of sport during the last couple of years. The approach presented here on the one hand shall demonstrate the performance that is induced by the ability of continuous learning. On the other hand, it is sketched in an outlook what could be future developments of this approach and what kind of basic work has to be done in order to better understand what is happening in a game.