@inproceedings{212725bb97834a01a9de332405b40ef0,
title = "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 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.",
author = "Mikhail Prokopenko and {Mahendra Rajah}, Piraveenan and Peter Wang",
year = "2005",
doi = "10.1007/11553090_89",
language = "English",
isbn = "3540288481",
volume = "3630 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "884--894",
editor = "Capcarrere, {Mathieu S.}",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "United States",
note = "8th European Conference on Advances in Artificial Life, ECAL 2005 ; Conference date: 05-09-2005 Through 09-09-2005",
}