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THE EFFECT OF NETWORK STRUCTURE ON DYNAMIC TEAM FORMATION IN MULTI-AGENT SYSTEMS

Authors

  • Matthew E. Gaston,

    1. Multi-Agent Planning and Learning (MAPLE) Laboratory, Department of Computer Science, University of Maryland Baltimore County, Baltimore, MD, USA
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  • Marie DesJardins

    1. Multi-Agent Planning and Learning (MAPLE) Laboratory, Department of Computer Science, University of Maryland Baltimore County, Baltimore, MD, USA
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Address correspondence to Marie desJardins, Multi-Agent Planning and Learning (MAPLE) Laboratory, Department of Computer Science, University of Maryland Baltimore County, Baltimore, MD 21250, USA; e-mail: mariedj@cs.umbc.edu

Abstract

Previous studies of team formation in multi-agent systems have typically assumed that the agent social network underlying the agent organization is either not explicitly described or the social network is assumed to take on some regular structure such as a fully connected network or a hierarchy. However, recent studies have shown that real-world networks have a rich and purposeful structure, with common properties being observed in many different types of networks. As multi-agent systems continue to grow in size and complexity, the network structure of such systems will become increasing important for designing efficient, effective agent communities.

We present a simple agent-based computational model of team formation, and analyze the theoretical performance of team formation in two simple classes of networks (ring and star topologies). We then give empirical results for team formation in more complex networks under a variety of conditions. From these experiments, we conclude that a key factor in effective team formation is the underlying agent interaction topology that determines the direct interconnections among agents. Specifically, we identify the property of diversity support as a key factor in the effectiveness of network structures for team formation. Scale-free networks, which were developed as a way to model real-world networks, exhibit short average path lengths and hub-like structures. We show that these properties, in turn, result in higher diversity support; as a result, scale-free networks yield higher organizational efficiency than the other classes of networks we have studied.

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