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Smart Shepherding: Towards Transparent Artificial Intelligence Enabled Human-Swarm Teams Read chapter Read first chapter

2021 | OriginalPaper | Chapter

6. Towards Ontology-Guided Learning for Shepherding

Author : Benjamin Campbell

Published in: Shepherding UxVs for Human-Swarm Teaming

Publisher: 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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previous chapter Mission Planning for Shepherding a Swarm of Uninhabited Aerial Vehicles
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Metadata
Title
Towards Ontology-Guided Learning for Shepherding
Author
Benjamin Campbell
Copyright Year
2021
Book
Shepherding UxVs for Human-Swarm Teaming

Print ISBN: 978-3-030-60897-2

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

Copyright Year: 2021

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

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