Basic Technologies for Knowledge Transfer in Intelligent Systems

Knowledge transfer is one of the most important mechanisms of human evolution. The ontogeny of humans enables them to act efficiently in a very dynamic environment. Thus, it would be highly desirable to enable "intelligent" artificial systems to behave in a similar way. This article introduces basic technologies that are needed for that purpose. With these technologies - components of a future knowledge transfer toolbox - it is possible to detect novel concepts that arise in the input space of a classifier or existing classification rules that become obsolete. Then, prototypes of new rules can be created automatically using an on-line clustering mechanism. These prototypes are compared to already existing rules, rated, and eventually accepted or discarded. In case of acceptance, a human expert labels the rules which are then both integrated into the "own" classifier and sent to other classifiers. Thus, knowledge transfer between "intelligent" artificial systems becomes possible and the overall system is provided with a new kind of self-optimization ability