On Convergence of Dynamic Cluster Formation in Multi-agent Networks

Abstract. 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 interagent communication space as the underlying parameter. The results indicate that the generalised “correlation entropy” (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.