Abstract
This paper investigates incremental multiagent learning in structured networks. Learning examples are incrementally distributed among the agents, and the objective is to build a common hypothesis that is consistent with all the examples present in the system, despite communication constraints. Recently, different mechanisms have been proposed that allow groups of agents to coordinate their hypotheses. Although these mechanisms have been shown to guarantee (theoretically) convergence to globally consistent states of the system, others notions of effectiveness can be considered to assess their quality. Furthermore, this guaranteed property should not come at the price of a great loss of efficiency (for instance a prohibitive communication cost). We explore these questions theoretically and experimentally (using different boolean formulas learning problems).
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Bourgne, G., El Fallah Seghrouchni, A., Maudet, N., Soldano, H. (2008). Multiagent Incremental Learning in Networks. In: Bui, T.D., Ho, T.V., Ha, Q.T. (eds) Intelligent Agents and Multi-Agent Systems. PRIMA 2008. Lecture Notes in Computer Science(), vol 5357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89674-6_14
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DOI: https://doi.org/10.1007/978-3-540-89674-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89673-9
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