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Particle Swarm Social Model for Group Social Learning in Adaptive Environment

  • Xiaohui Cui
  • Laura L. Pullum
  • Jim Treadwell
  • Robert M. Patton
  • Thomas E. Potok

Abstract

This report presents a study of integrating particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the social learning of self-organized groups and their collective searching behavior in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social learning for a dynamic environment. The research provides a platform for understanding and insights into knowledge discovery and strategic search in human self-organized social groups, such as human communities.

Keywords

Particle Swarm Optimization Particle Swarm Social Learning Agent Base Simulation Particle Swarm Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Xiaohui Cui
    • 1
  • Laura L. Pullum
    • 2
  • Jim Treadwell
    • 1
  • Robert M. Patton
    • 1
  • Thomas E. Potok
    • 1
  1. 1.Computational Sciences and Engineering DivisionOak Ridge National LaboratoryOak Ridge
  2. 2.Lockheed Martin CorporationSt. Paul

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