Abstract
One of the subfields of artificial intelligence (AI) technology is machine learning. The basic goal is to develop robots that can learn in the same way that humans can. “Learning” here refers to seeing, comprehending, and denoting facts regarding a statistical phenomenon. Synonyms for “learning” include “observe,” “represent information,” and “understand some statistical phenomena.” Swarms of bio-inspired robots cover a wide range of dynamics and collective behaviors. The categorization of swarm behavior from given certain agent measurements of a swarm at a specific time instance is a significant challenge. In reality, only a few agents’ data are available, resulting in minimal agent samples for classification. We solve these issues in this study by applying machine learning to represent a swarm’s collective movements. We apply various machine learning algorithms to classify the swarm behavior in terms of flocking, aligned and grouped, or non-flocking, non-aligned, and non-grouped.
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Tiwari, R.G., Yadav, S.K., Misra, A., Sharma, A. (2023). Classification of Swarm Collective Motion Using Machine Learning. In: Bhattacharyya, S., Banerjee, J.S., Köppen, M. (eds) Human-Centric Smart Computing. Smart Innovation, Systems and Technologies, vol 316. Springer, Singapore. https://doi.org/10.1007/978-981-19-5403-0_14
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DOI: https://doi.org/10.1007/978-981-19-5403-0_14
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