Dynamic Evolution of Role Taxonomies through Multidimensional Clustering in Multiagent Organizations

This paper addresses the problem of exploring how organizational structures may evolve over time using the information from the agents' trust models. We present a mechanism based on clustering techniques capable of detecting behavioural patterns in organizational multi-agent systems, thereby identifying new roles that dynamically extend the role taxonomy. We present experimental results showing that this extension leads to an improvement of the agents' decision making processes when compared to static organizational structures.