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 multiagent 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.
This paper has been prepared with a specific purpose in mind — to provide a basis for the Panel on Learning in Distributed Systems. It was edited by P.Brazdil on the basis of the individual contributions received and/or papers made available. The names of the authors are shown in the alphabetic order.
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Brazdil, P., Gams, M., Sian, S., Torgo, L., van de Velde, W. (1991). Learning in distributed systems and multi-agent environments. In: Kodratoff, Y. (eds) Machine Learning — EWSL-91. EWSL 1991. Lecture Notes in Computer Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017034
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DOI: https://doi.org/10.1007/BFb0017034
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