Knowledge Self-Adaptive Multi-Agent Learning

Abstract

In this paper concepts of a starting Doctoral Dissertation are presented, discussing the question how agents constructed according to Organic Computing methodologies can autonomously identify Knowledge Sources and adapt them to their learning procedure. Achieving this, the fields of Multi-Agent Learning, Organic Computing, Transfer Learning, and Online Learning are combined to an unified architecture. The focus of the work is on the real-time evaluation of knowledge sources. In order to show the practical use case of such systems, the author presents two scenarios. The first, collaborative crawling, is an information retrieval task, hence it deals with knowledge distributed over multiple websites. Whereas the latter is designed to run in a virtual space, the second, denoted as machine park collaboration, can be implemented in industrial 4.0 fields of the real world.