Abstract
This paper describes an evolutionary approach to the design of controllers for a team of collective agents. The agents are able to perform ant-like annular sorting, similar to the sorting behaviour seen in the ant species Temnothorax albipennis. While most previous research on ant-like sorting has used hard-wired rules, this study uses neural network controllers designed by artificial evolution. The agents have very simple and purely local sensory capabilities, and can only communicate through stigmergy. Experiments are performed in simulation. The evolved behaviours are presented, analyzed, and compared to previous research on ant-like annular sorting. The results show that artificial evolution is able to create efficient, simple, and scalable controllers able to perform annular sorting of three object types.
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Vik, A.H. (2005). Evolving Annular Sorting in Ant-Like Agents. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science(), vol 3630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553090_60
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DOI: https://doi.org/10.1007/11553090_60
Publisher Name: Springer, Berlin, Heidelberg
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