On Convergence of Dynamic Cluster Formation in Multi-agent Networks

  • Mikhail Prokopenko
  • Piraveenan Mahendra Rajah
  • Peter Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3630)


Efficient hierarchical architectures for reconfigurable and adaptive multi-agent networks require dynamic cluster formation among the set of nodes (agents). In the absence of centralised controllers, this process can be described as self-organisation of dynamic hierarchies, with multiple cluster-heads emerging as a result of inter-agent communications. Decentralised clustering algorithms deployed in multi-agent networks are hard to evaluate precisely for the reason of the diminished predictability brought about by self-organisation. In particular, it is hard to predict when the cluster formation will converge to a stable configuration. This paper proposes and experimentally evaluates a predictor for the convergence time of cluster formation, based on a regularity of the inter-agent communication space as the underlying parameter. The results indicate that the generalised “correlation entropy” K 2 (a lower bound of Kolmogorov-Sinai entropy) of the volume of the inter-agent communications can be correlated with the time of cluster formation, and can be used as its predictor.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Mikhail Prokopenko
    • 1
  • Piraveenan Mahendra Rajah
    • 2
  • Peter Wang
    • 1
  1. 1.CSIRO Information and Communication Technology CentreNorth RydeAustralia
  2. 2. North AdelaideAustralia

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