Group Formation Among Decentralized Autonomous Agents

E.F.Y.L. Ogston, M.R. van Steen, F.M. Brazier

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This paper examines a method of clustering within a fully decentralized multi-agent system. Our goal is to group agents with similar objectives or data, as is done in traditional clustering. However, we add the additional constraint that agents must remain in place on a network, instead of first being collected into a centralized database. To do this, we connect agents in a random overlay network and have them search in a peer-to-peer fashion for other similar agents. We thus aim to tackle the basic clustering problem on an Internet scale, and create a method by which agents themselves can be grouped, forming coalitions. In order to investigate the feasibility of this decentralized approach, this paper presents simulation experiments that look into the quality of the clusters discovered. First, the clusters found by the agent method are compared to those created by k-means clustering for two-dimensional spatial data points. Results show that the decentralized agent method produces a better clustering than the centralized k-means algorithm, placing 95% to 99% of points correctly. A further experiment explores how agents can be used to cluster a straightforward text document set, demonstrating that agents can discover clusters and keywords that are reasonable estimates of those identified by the central word vector space approach.
Original languageEnglish
Pages (from-to)953-970
JournalApplied Artificial Intelligence
Volume18
Issue number9--10
DOIs
Publication statusPublished - 2004

Bibliographical note

steen2004.13

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