Restricted On-line Learning in Real-world Systems

Systems capable of adapting to changing conditions have gained increasing attention in the last decade. Typically, vast situation and configuration spaces do not allow for using a predefined set of adaptation policies. Based on the principles of Organic Computing, a 3-layered learning architecture has been developed which is capable of coping with the problem by enabling self-adaptation and self-improvement. A major focus has been set on developing safety-based and efficient machine learning concepts founding on evolutionary search heuristics and rule-based learning. The general design has been successfully applied to safety-critical real-world applications like urban traffic control and data communication protocols. This paper investigates the question for which class of technical systems the design is applicable. Thus, a generalised model based on mathematical functions is introduced and evaluated. The evaluation demonstrates that the approach works well for systems where the configuration spaces are steadily representable by functions of the situation space. This statement holds even in the presence of noise.