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2021 | OriginalPaper | Buchkapitel

6. Towards Ontology-Guided Learning for Shepherding

verfasst von : Benjamin Campbell

Erschienen in: Shepherding UxVs for Human-Swarm Teaming

Verlag: Springer International Publishing

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Abstract

Shepherding offers an exciting application for machine learning research. Shepherding tasks are scalable in terms of both complexity and dimension. This scalability supports investigations into the generality of learned multi-agent solutions. Shepherding is also valuable for the study of how multi-agent learning systems transition from simulation to physical systems. This chapter reviews previous learning strategies for shepherding and highlights the advantages of applying prior knowledge to the design of learning systems for shepherding. It presents ontology guided learning, a hybrid learning approach to learning. Ontology guided learning will enable the application of symbolic prior knowledge to non-symbolic learning systems. This will allow a non-symbolic system to reason on abstract concepts, reduce dimensionality by partitioning the state and action space, increase transparency and allow learning to focus on the parametric rather than semantic parts of the problem, where it will likely be most effective. This chapter concludes by describing how ontology guided learning could be applied to the shepherding problem.

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Nächstes Kapitel Activity Recognition for Shepherding

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Titel: Towards Ontology-Guided Learning for Shepherding
verfasst von: Benjamin Campbell
Verlag: Springer International Publishing
Buch: Shepherding UxVs for Human-Swarm Teaming
Print ISBN: 978-3-030-60897-2

Electronic ISBN: 978-3-030-60898-9

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-60898-9_6

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