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.

• Type: Conference
• Year: 1998
• Where: AGENTS
• Authors: Gerhard Weiß
• Keywords: AGENTS 1998, Artificial Intelligence, Distributed Artificial Intelligence, Multiagent Learning Perspective, Security Privacy