A Multiagent Perspective of Parallel and Distributed Machine Learning

Parallel and distributed information processing systems play an increasingly important role in artificial intelligence and computer science. In this article an approach to learning in such systems is described that follows the multiagent learning perspective known from the field of distributed artificial intelligence. As an evaluation task the job assignment problem is chosen. This is an NP problem which is relevant to many industrial application domains. Experimental results are presented that illustrate the benefits of the proposed approach. 1 Introduction The past years have witnessed a steadily growing interest in parallel and distributed information processing systems in artificial intelligence and computer science. This interest has led to new research and application activities in areas like parallel and distributed algorithms, concurrent programming, distributed database systems, and parallel and distributed hardware architectures. Three basic, interrelated reasons for this i..