Abstract
Many real world situations are currently being modelled as a set of cooperating intelligent agents. Trying to introduce learning into such a system requires dealing with the existence of multiple autonomous agents. The inherent distribution means that effective learning has to be based on a cooperative framework in which each agent contributes its part. In this paper we look at the issues in multi-agent machine learning and examine what effect the presence of multiple agents has on current learning methodologies. We describe a model for cooperative learning based on structured dialogue between the agents. MALE is an implementation of this model and we describe some results from it.
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© 1991 Springer-Verlag Berlin Heidelberg
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Sian, S.S. (1991). Extending learning to multiple agents: Issues and a model for multi-agent machine learning (MA-ML). 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/BFb0017036
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DOI: https://doi.org/10.1007/BFb0017036
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