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Learning in a fixed or evolving network of agents

Gauvain Bourgne Amal El Fallah-Seghrouchni 1 Henry Soldano
1 SMA - Systèmes Multi-Agents
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This paper investigates incremental multiagent learning in static or evolving 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, a first mechanism was proposed to deal with static networks, but its accuracy was reduced in some topologies. We propose here several possible improvements of this mechanism, whose different behaviors with respect to some efficiency requirements (redundancy, computational cost and communicational cost) are experimentally investigated. Then, we provide an experimental analysis of some variants for evolving networks.
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Submitted on : Wednesday, March 30, 2016 - 4:49:56 PM
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Gauvain Bourgne, Amal El Fallah-Seghrouchni, Henry Soldano. Learning in a fixed or evolving network of agents. IEEE Conference on Intelligent Agent technology IAT'2009, 2009, Milan, Italy. pp.549-556, ⟨10.1109/WI-IAT.2009.210⟩. ⟨hal-01295287⟩



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