Collaborative Learning by Knowledge Exchange

Abstract

Humans act efficiently in a dynamic environment by learning from each other. Thus, it would be highly desirable to enable intelligent distributed systems, e.g., multi-agent systems, smart sensor networks, or teams of robots, to behave in a way which follows that biological archetype. The constituents of a such a distributed system may learn in a collaborative way by communicating locally learned classification rules, for instance. This article first gives an overview of the techniques that we have developed for knowledge exchange. Then, their application is demonstrated in a realistic scenario, collaborative detection of attacks to a computer network.