On Convergence of Dynamic Cluster Formation in Multi-agent Networks
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- Prokopenko M., Rajah P.M., Wang P. (2005) On Convergence of Dynamic Cluster Formation in Multi-agent Networks. In: Capcarrère M.S., Freitas A.A., Bentley P.J., Johnson C.G., Timmis J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science, vol 3630. Springer, Berlin, Heidelberg
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” K2 (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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