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Robust Intelligence and Trust in Autonomous Systems

Overview of attention for book
Cover of 'Robust Intelligence and Trust in Autonomous Systems'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Towards Modeling the Behavior of Autonomous Systems and Humans for Trusted Operations
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    Chapter 3 Learning Trustworthy Behaviors Using an Inverse Trust Metric
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    Chapter 4 The “Trust V”: Building and Measuring Trust in Autonomous Systems
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    Chapter 5 Big Data Analytic Paradigms: From Principle Component Analysis to Deep Learning
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    Chapter 6 Artificial Brain Systems Based on Neural Network Discrete Chaotic Dynamics. Toward the Development of Conscious and Rational Robots
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    Chapter 7 Modeling and Control of Trust in Human-Robot Collaborative Manufacturing
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    Chapter 8 Investigating Human-Robot Trust in Emergency Scenarios: Methodological Lessons Learned
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    Chapter 9 Designing for Robust and Effective Teamwork in Human-Agent Teams
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    Chapter 10 Measuring Trust in Human Robot Interactions: Development of the “ Trust Perception Scale-HRI ”
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    Chapter 11 Methods for Developing Trust Models for Intelligent Systems
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    Chapter 12 The Intersection of Robust Intelligence and Trust: Hybrid Teams, Firms and Systems
Attention for Chapter 4: The “Trust V”: Building and Measuring Trust in Autonomous Systems
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Citations

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9 Mendeley
Summary Dimensions citations
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Chapter title
The “Trust V”: Building and Measuring Trust in Autonomous Systems
Chapter number 4
Book title
Robust Intelligence and Trust in Autonomous Systems
Published by
Springer, Boston, MA, January 2016
DOI 10.1007/978-1-4899-7668-0_4
Book ISBNs
978-1-4899-7666-6, 978-1-4899-7668-0
Authors

Gari Palmer, Anne Selwyn, Dan Zwillinger

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 33%
Researcher 2 22%
Student > Postgraduate 1 11%
Student > Ph. D. Student 1 11%
Unknown 2 22%
Readers by discipline Count As %
Engineering 3 33%
Computer Science 1 11%
Business, Management and Accounting 1 11%
Social Sciences 1 11%
Design 1 11%
Other 0 0%
Unknown 2 22%

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