Using Graph Theory to Produce Emergent Behaviour in Agent-Based Systems

Gower-Winter, B. and Nitschke, G. (2023) Using Graph Theory to Produce Emergent Behaviour in Agent-Based Systems, Proceedings of IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2023), Mexico City, Mexico, IEEE Press.

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Abstract

Cooperation is a defining trait of Multi-Agent Systems. At the centre of these systems lies a communication network which governs how information flows from one agent to the next. However, the design of these networks is often overlooked despite the profound impact it can have on both the task performance of the agents and the emergent phenomena they produce. In this work we aim to illustrate this by investigating whether network centrality impacts the task performance and emergent inequality (unequal distribution of resources) of resource gathering agents. We achieve this by constructing several communication networks with increasing centrality and use them with an Agent-Based Model called GATHER. Our results indicate that as the variance of the population’s centrality increases, the task performance of an agent population will decrease. Furthermore, we demonstrate that simply changing the centrality of the network can produce distinct results and emergent phenomena (inequality or the lack thereof in our case). We then further support this claim by increasing the reciprocity of one of our communication networks which results in a system with greater task performance and significantly lower inequality, further illustrating the impact communication network topology can have on Multi-Agent Systems.

Item Type:	Conference paper
Subjects:	Computing methodologies > Artificial intelligence
Date Deposited:	10 Nov 2023 14:47
Last Modified:	10 Nov 2023 14:47
URI:	https://pubs.cs.uct.ac.za/id/eprint/1621

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