Abstract
This paper explores the differential impact of multi-agent system heterogeneity in the context of an idealised herding task. In simulation, a team of simple herders must move a flock towards a target location in a continuous 2d space. Flock heterogeneity is controlled by dividing the flock into a number of non-overlapping social groups that influence sheep movement. Results demonstrate that increasing system heterogeneity (i.e., the number of different social groups) reduces herding performance when social groups are self-attracting, but conversely, the same increase in system heterogeneity can increase herding performance when groups are other-attracting. Implications for designing heterogeneous multi-agent systems are considered.
This work was funded and delivered in partnership between the Thales Group and the University of Bristol, and with the support of the UK Engineering and Physical Sciences Research Council Grant Award EP/R004757/1 entitled ‘Thales-Bristol Partnership in Hybrid Autonomous Systems Engineering (T-B PHASE)’.
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Bennett, C., Bullock, S., Lawry, J. (2021). Demonstrating the Differential Impact of Flock Heterogeneity on Multi-agent Herding. In: Fox, C., Gao, J., Ghalamzan Esfahani, A., Saaj, M., Hanheide, M., Parsons, S. (eds) Towards Autonomous Robotic Systems. TAROS 2021. Lecture Notes in Computer Science(), vol 13054. Springer, Cham. https://doi.org/10.1007/978-3-030-89177-0_15
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