Abstract
This paper presents an algorithm for a decentralized self-organizing map. With the explosion in the availability of robotics platforms, and their increasing application to multi-agent systems and robot swarms, there is a need for a new generation of machine learning algorithms that can exploit the distributed nature of sensing and processing that can be achieved using such platforms. In this paper we examine one such algorithm for decentralized pattern recognition, assuming sensors and processors are distributed across multiple agents: a decentralized self-organizing map. We examine the proposed algorithm under a range of conditions. This includes numbers of agents, communication topologies between agents and number of encounters between agents, simulating their presence in smaller or larger spaces. We demonstrate the conditions in which our decentralised self-organizing map can achieve comparable learning performance to a centralized self-organizing map on a range of synthetic datasets.
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Khan, M.M., Kasmarik, K., Garratt, M. (2022). Decentralizing Self-organizing Maps. In: Long, G., Yu, X., Wang, S. (eds) AI 2021: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13151. Springer, Cham. https://doi.org/10.1007/978-3-030-97546-3_39
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DOI: https://doi.org/10.1007/978-3-030-97546-3_39
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