Multiagent Incremental Learning in Networks

  • Gauvain Bourgne
  • Amal El Fallah Seghrouchni
  • Nicolas Maudet
  • Henry Soldano
Conference paper

DOI: 10.1007/978-3-540-89674-6_14

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5357)
Cite this paper as:
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

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).

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gauvain Bourgne
    • 1
  • Amal El Fallah Seghrouchni
    • 2
  • Nicolas Maudet
    • 1
  • Henry Soldano
    • 3
  1. 1.LAMSADE, Université Paris-DauphineParis Cedex 16France
  2. 2.LIP6, Université Pierre and Marie CurieParisFrance
  3. 3.LIPN, Université Paris-NordVilletaneuseFrance

Personalised recommendations