Learning in Distributed Systems and Multi-Agent Environments
P. Brazdil, M. Gams, S. Sian, L. Torgo and W. van de Velde
1991
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 diffenrent 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.