id: 05821866
dt: a
an: 05821866
au: Morihiro, Koichiro; Matsui, Nobuyuki; Isokawa, Teijiro; Nishimura, Haruhiko
ti: Reinforcement learning scheme for grouping and characterization of
    multi-agent network.
so: Setchi, Rossitza (ed.) et al., Knowledge-based and intelligent information
    and engineering systems. 14th international conference, KES 2010,
    Cardiff, UK, September 8‒10, 2010. Proceedings, Part III. Berlin:
    Springer (ISBN 978-3-642-15392-1/pbk). Lecture Notes in Computer
    Science 6278. Lecture Notes in Artificial Intelligence, 592-601 (2010).
py: 2010
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-15393-8_66
ab: Summary: Several models have been proposed for describing grouping behavior
    such as bird flocking, terrestrial animal herding, and fish schooling.
    In these models, a fixed rule has been imposed on each individual a
    priori for its interactions in a reductive and rigid manner. We have
    proposed a new framework for self-organized grouping of agents by
    reinforcement learning. It is important to introduce a learning scheme
    for developing collective behavior in artificial autonomous distributed
    systems. This scheme can be expanded to cases in which predators are
    present. We integrated grouping and anti-predator behaviors into our
    proposed scheme. The behavior of agents is demonstrated and evaluated
    in detail through computer simulations, and their grouping and
    anti-predator behaviors developed as a result of learning are shown to
    be diverse and robust by changing some parameters of the scheme. In
    this study, we investigate the network structure of agents in the
    process of learning these behaviors. From the view point of the complex
    network, the average shortest path length and clustering coefficient
    are evaluated through computer simulations.
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