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Activity Recognition for Shepherding

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Shepherding UxVs for Human-Swarm Teaming

Part of the book series: Unmanned System Technologies ((UST))

Abstract

Activity recognition for shepherding is a way for an artificial intelligence system to learn and understand shepherding behaviours. The problem we describe is one of recognising behaviours within a shepherding environment, where a cognitive agent (the shepherd) influences agents within the system (sheep) through a shepherding actuator (sheepdog), to achieve an intent. Shepherding is pervasive in everyday life with AI agents, collections of animals, and humans all partaking in different forms. Activity recognition in this context is the generation of a transformation from sensor stream data to the perceived behaviour of an agent under observation from the perspective of an external observer. We present a method of classifying behaviour through the use of spatial data and codify action, behaviour, and intent states through a multi-level classification mapping process.

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    Adam J. Hepworth

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    Hussein A. Abbass

  2. University of New South Wales, Canberra, ACT, Australia

    Robert A. Hunjet

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Hepworth, A.J. (2021). Activity Recognition for Shepherding. In: Abbass, H.A., Hunjet, R.A. (eds) Shepherding UxVs for Human-Swarm Teaming. Unmanned System Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-60898-9_7

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