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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Prokopenko, M., Mahendra Rajah, P., & Wang, P. (2005). On convergence of dynamic cluster formation in multi-agent networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3630 LNAI, pp. 884–894). https://doi.org/10.1007/11553090_89
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