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Shepherding UxVs for Human-Swarm Teaming = An Artificial Intelligence Approach to Unmanned X Vehicles /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Shepherding UxVs for Human-Swarm Teaming/ edited by Hussein A. Abbass, Robert A. Hunjet.
Reminder of title:	An Artificial Intelligence Approach to Unmanned X Vehicles /
other author:	Abbass, Hussein A.
Description:	XX, 330 p. 88 illus., 44 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computer engineering. -
Online resource:	https://doi.org/10.1007/978-3-030-60898-9
ISBN:	9783030608989

Shepherding UxVs for Human-Swarm Teaming = An Artificial Intelligence Approach to Unmanned X Vehicles /
Shepherding UxVs for Human-Swarm TeamingAn Artificial Intelligence Approach to Unmanned X Vehicles /[electronic resource] :edited by Hussein A. Abbass, Robert A. Hunjet. - 1st ed. 2021. - XX, 330 p. 88 illus., 44 illus. in color.online resource. - Unmanned System Technologies,2523-3742. - Unmanned System Technologies,.

Introduction -- Introduction to Shepherding -- Introduction to Human-Swarm Teaming -- Swarm Shepherding on Ground -- Swarm Shepherding in Air -- Swarm Shepherding in Air Traffic Control -- Swarm Shepherding in Sea -- Genetic Algorithms for Optimizing Swarm Shepherding -- Reinforcement Learning for Swarm Shepherding -- Learning Classifier Systems for Swarm Shepherding -- Transparent Learning for Swarm Shepherding -- Ontology-guided Learning for Swarm Shepherding -- Mission Planning for Swarm Shepherding -- Real-Time Human Performance Analysis for Human-Swarm Teaming -- Trust for Human-Swarm Teaming -- Machine Education of Smart Shepherds -- The effect of communication range limits on shepherding performance -- Controlling the controllers: the multi shepherd swarm control problem -- Conclusion.

This book draws inspiration from natural shepherding, whereby a farmer utilizes sheepdogs to herd sheep, to inspire a scalable and inherently human friendly approach to swarm control. The book discusses advanced artificial intelligence (AI) approaches needed to design smart robotic shepherding agents capable of controlling biological swarms or robotic swarms of unmanned vehicles. These smart shepherding agents are described with the techniques applicable to the control of Unmanned X Vehicles (UxVs) including air (unmanned aerial vehicles or UAVs), ground (unmanned ground vehicles or UGVs), underwater (unmanned underwater vehicles or UUVs), and on the surface of water (unmanned surface vehicles or USVs). This book proposes how smart ‘shepherds’ could be designed and used to guide a swarm of UxVs to achieve a goal while ameliorating typical communication bandwidth issues that arise in the control of multi agent systems. The book covers a wide range of topics ranging from the design of deep reinforcement learning models for shepherding a swarm, transparency in swarm guidance, and ontology-guided learning, to the design of smart swarm guidance methods for shepherding with UGVs and UAVs. The book extends the discussion to human-swarm teaming by looking into the real-time analysis of human data during human-swarm interaction, the concept of trust for human-swarm teaming, and the design of activity recognition systems for shepherding. Presents a comprehensive look at human-swarm teaming; Tackles artificial intelligence techniques for swarm guidance; Provides artificial intelligence techniques for real-time human performance analysis.

ISBN: 9783030608989

Standard No.: 10.1007/978-3-030-60898-9doiSubjects--Topical Terms:

569006
Computer engineering.

LC Class. No.: TK7885-7895

Dewey Class. No.: 621.38

Shepherding UxVs for Human-Swarm Teaming = An Artificial Intelligence Approach to Unmanned X Vehicles /
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505 0 $a Introduction -- Introduction to Shepherding -- Introduction to Human-Swarm Teaming -- Swarm Shepherding on Ground -- Swarm Shepherding in Air -- Swarm Shepherding in Air Traffic Control -- Swarm Shepherding in Sea -- Genetic Algorithms for Optimizing Swarm Shepherding -- Reinforcement Learning for Swarm Shepherding -- Learning Classifier Systems for Swarm Shepherding -- Transparent Learning for Swarm Shepherding -- Ontology-guided Learning for Swarm Shepherding -- Mission Planning for Swarm Shepherding -- Real-Time Human Performance Analysis for Human-Swarm Teaming -- Trust for Human-Swarm Teaming -- Machine Education of Smart Shepherds -- The effect of communication range limits on shepherding performance -- Controlling the controllers: the multi shepherd swarm control problem -- Conclusion.
520 $a This book draws inspiration from natural shepherding, whereby a farmer utilizes sheepdogs to herd sheep, to inspire a scalable and inherently human friendly approach to swarm control. The book discusses advanced artificial intelligence (AI) approaches needed to design smart robotic shepherding agents capable of controlling biological swarms or robotic swarms of unmanned vehicles. These smart shepherding agents are described with the techniques applicable to the control of Unmanned X Vehicles (UxVs) including air (unmanned aerial vehicles or UAVs), ground (unmanned ground vehicles or UGVs), underwater (unmanned underwater vehicles or UUVs), and on the surface of water (unmanned surface vehicles or USVs). This book proposes how smart ‘shepherds’ could be designed and used to guide a swarm of UxVs to achieve a goal while ameliorating typical communication bandwidth issues that arise in the control of multi agent systems. The book covers a wide range of topics ranging from the design of deep reinforcement learning models for shepherding a swarm, transparency in swarm guidance, and ontology-guided learning, to the design of smart swarm guidance methods for shepherding with UGVs and UAVs. The book extends the discussion to human-swarm teaming by looking into the real-time analysis of human data during human-swarm interaction, the concept of trust for human-swarm teaming, and the design of activity recognition systems for shepherding. Presents a comprehensive look at human-swarm teaming; Tackles artificial intelligence techniques for swarm guidance; Provides artificial intelligence techniques for real-time human performance analysis.
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