Abstract
In this paper the conception of evolutionary multi-agent model for knowledge acquisition has been introduced. The basic idea of the proposed solution is to use the multi-agent paradigm in order to enable the integration and co-operation of different knowledge acquisition and representation methods. At the single-agent level the reinforcement learning process is realized, while the obtained knowledge is represented as the set of simple decision rules. One of the conditions of effective agent learning is the optimization of the set of it’s features (parameters) that are represented by the genotype’s vector. The evolutionary optimization runs at the level of population of agents.
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Froelich, W. (2005). Evolutionary Multi-Agent Model for Knowledge Acquisition. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_66
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DOI: https://doi.org/10.1007/3-540-32392-9_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25056-2
Online ISBN: 978-3-540-32392-1
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