Simulation of learning and planning by a novel architecture for cognitive technical systems

A novel architecture for Cognitive Technical Systems with a homogeneous knowledge representation for all cognitive functions is presented. The approach is methodically based on Situation-Operator-Modeling and implemented with high-level Petri Nets. It is not restricted to certain application fields and can be combined with other AI methods. By the combination of several instances of the architecture, the complexity of the represented interaction can be reduced by a problem-oriented encapsulation of different action spaces. The contribution is focused on learning of interaction in and with the outside world and its inner structure, which is illustrated by the control of an arcade game.