LEARNING IN DISTRIBUTED SYSTEMS
AND
MULTI-AGENT ENVIRONMENTS^*

Abstract

The paper begins with the discussion on why we should be concerned with machine learning in the context of distributed AI. The rest of the paper is dedicated to various problems of multi-agent learning. First, a common framework for comparing different existing systems is presented. It is pointed out that it is useful to distinguish when the individual agents communicate. Some systems communicate during the learning phase, others during the problem solving phase, for example. It is also important to consider how, that is in what language, the communication is established. The paper analyses several systems in this framework. Particular attention is paid to previous work done by the authors in this area. The paper covers use of redundant knowledge, knowledge integration, evaluation of hypothesis by a community of agents and resolution of language differences between agents.

Keywords: learning in distributed systems, multi-agent learning, evaluation of hypotheses, knowledge integration, use of redundant knowledge, resolution of language differences.

• 1. Introduction
• 2. Autonomous Agent Learning
• 3. Multi-Agent Learning Systems
• 4. Some Aspects of Communication between Agents
• 5. Role of Learning in a Community of Agents

• Contents