Self organization map for clustering and classification in the ecology of agent organizations

Abstract

Development of computational agent organizations or “societies” has become the domiant computing paradigm in the arena of Distributed Artificial Intelligence, and many foreseeable future applications need agent organizations, in which diversified agents cooperate in a distributed manner, forming teams. In such scenarios, the agents would need to know each other in order to facilitate the interactions. Moreover, agents in such an environment are not statically defined in advance but they can adaptively enter and leave an organization. This begs the question of how agents locate each other in order to cooperate in achieving organizational goals. Locating agents is a quite challenging task, especially in organizations that involve a large number of agents and where the resource avaiability is intermittent. The authors explore here an approach based on self-organization map (SOM) which will serve as a clustering method in the light of the knowledge gathered about various agents. The approach begins by categorizing agents using a selected set of agent properties. These categories are used to derive various ranks and a distance matrix. The SOM algorithm uses this matrix as input to obtain clusters of agents. These clusters reduce the search space, resulting in a relatively short agent search time.