Abstract
A population of agents faced with difficult on-line pattern recognition tasks can benefit from peer-to-peer communication where significant individual experiences are made available to the group. We investigate the interaction between agent architecture, knowledge representation and communication strategy for cooperating agents in an unstable environment with many different niches and discuss application scenarios. Back-propagation artificial neural networks, decision trees and support vector machines are considered as representative core algorithms for the agents. Numerical experiments compare different communication strategies. It is found that support vector machines, where knowledge is represented as records of significant inputs, are particularly well suited for efficient collaborative communication.
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Jändel, M. (2009). Cooperating Classifiers. In: Krasnogor, N., Melián-Batista, M.B., Pérez, J.A.M., Moreno-Vega, J.M., Pelta, D.A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2008). Studies in Computational Intelligence, vol 236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03211-0_18
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DOI: https://doi.org/10.1007/978-3-642-03211-0_18
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