Table of Contents: Foundations of Trusted Autonomy.

Foundations of Trusted Autonomy.

Bibliographic Details
Main Author:	Abbass, Hussein A.
Other Authors:	Scholz, Jason., Reid, Darryn J.
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing AG, 2018.
Edition:	1st ed.
Series:	Studies in Systems, Decision and Control Series
Subjects:	Electronic books.
Online Access:	Click to View

Table of Contents:

Intro
Foreword
Preface
Acknowledgements
Contents
Contributors
1 Foundations of Trusted Autonomy: An Introduction
1.1 Autonomy
1.2 Trust
1.3 Trusted Autonomy
Autonomy
2 Universal Artificial Intelligence
2.1 Introduction
2.2 Background and History of AI
2.3 Universal Artificial Intelligence
2.3.1 Framework
2.3.2 Learning
2.3.3 Goal
2.3.4 Planning
2.3.5 AIXI
Putting It All Together
2.4 Approximations
2.4.1 MC-AIXI-CTW
2.4.2 Feature Reinforcement Learning
2.4.3 Model-Free AIXI
2.4.4 Deep Learning
2.5 Fundamental Challenges
2.5.1 Optimality and Exploration
2.5.2 Asymptotically Optimal Agents
2.6 Predicting and Controlling Behaviour
2.6.1 Self-Modification
2.6.2 Counterfeiting Reward
2.6.3 Death and Self-Preservation
2.7 Conclusions
References
3 Goal Reasoning and Trusted Autonomy
3.1 Introduction
3.2 Goal-Driven Autonomy Models
3.2.1 Goal-Driven Autonomy
3.2.2 Goal Selection
3.2.3 An Application for Human-Robot Teaming
3.3 Goal Refinement
3.3.1 Goal Lifecycle
3.3.2 Guaranteeing the Execution of Specified Behaviors
3.3.3 A Distributed Robotics Application
3.4 Future Topics
3.4.1 Adaptive Autonomy and Inverse Trust
3.4.2 Rebel Agents
3.5 Conclusion
References
4 Social Planning for Trusted Autonomy
4.1 Introduction
4.2 Motivation and Background
4.2.1 Automated Planning
4.2.2 From Autistic Planning to Social Planning
4.3 Social Planning
4.3.1 A Formal Model for Multi-agent Epistemic Planning
4.3.2 Solving Multi-agent Epistemic Planning Problems
4.4 Social Planning for Human Robot Interaction
4.4.1 Search and Rescue
4.4.2 Collaborative Manufacturing
4.5 Discussion
References
5 A Neuroevolutionary Approach to Adaptive Multi-agent Teams
5.1 Introduction.
5.2 The Legion II Game
5.2.1 The Map
5.2.2 Units
5.2.3 Game Play
5.2.4 Scoring the Game
5.3 Agent Control Architectures
5.3.1 Barbarian Sensors and Controllers
5.3.2 Legion Sensors and Controllers
5.4 Neuroevolution With Enforced Sub-Populations (ESP)
5.5 Experimental Methodology
5.5.1 Repeatable Gameplay
5.5.2 Training
5.5.3 Testing
5.6 Experiments
5.6.1 Learning the Division of Labor
5.6.2 Run-Time Readaptation
5.7 Discussion
5.8 Conclusions
References
6 The Blessing and Curse of Emergence in Swarm Intelligence Systems
6.1 Introduction
6.2 Emergence in Swarm Intelligence
6.3 The `Blessing' of Emergence
6.4 The `Curse' of Emergence
6.5 Taking Advantage of the Good While Avoiding the Bad
6.6 Conclusion
References
7 Trusted Autonomous Game Play
7.1 Introduction
7.2 TA Game AI
7.3 TA Game
7.4 TA Game Communities
7.5 TA Mixed Reality Games
7.6 Discussion: TA Games
References
Trust
8 The Role of Trust in Human-Robot Interaction
8.1 Introduction
8.2 Conceptualization of Trust
8.3 Modeling Trust
8.4 Factors Affecting Trust
8.4.1 System Properties
8.4.2 Properties of the Operator
8.4.3 Environmental Factors
8.5 Instruments for Measuring Trust
8.6 Trust in Human Robot Interaction
8.6.1 Performance-Based Interaction: Humans Influencing Robots
8.6.2 Social-Based Interactions: Robots Influencing Humans
8.7 Conclusions and Recommendations
References
9 Trustworthiness of Autonomous Systems
9.1 Introduction
9.1.1 Autonomous Systems
9.1.2 Trustworthiness
9.2 Background
9.3 Who or What Is Trustworthy?
9.4 How do We Know Who or What Is Trustworthy
9.4.1 Implicit Justifications of Trust
9.4.2 Explicit Justifications of Trust
9.4.3 A Cognitive Model of Trust and Competence.
9.4.4 Trustworthiness and Risk
9.4.5 Summary
9.5 What or Who Should We Trust?
9.6 The Value of Trustworthy Autonomous Systems
9.7 Conclusion
References
10 Trusted Autonomy Under Uncertainty
10.1 Trust and Uncertainty
10.1.1 What Is Trust?
10.1.2 Trust and Distrust in HRI
10.2 Trust and Uncertainty
10.2.1 Trust and Distrust Entail Unknowns
10.2.2 What Is Being Trusted
What Is Uncertain?
10.2.3 Trust and Dilemmas
10.3 Factors Affecting Human Reactivity to Risk and Uncertainty, and Trust
10.3.1 Kinds of Uncertainty, Risks, Standards, and Dispositions
10.3.2 Presumptive and Organizational-Level Trust
10.3.3 Trust Repair
10.4 Concluding Remarks
References
11 The Need for Trusted Autonomy in Military Cyber Security
11.1 Introduction
11.2 Cyber Security
11.3 Challenges and the Potential Application of Trusted Autonomy
11.4 Conclusion
References
12 Reinforcing Trust in Autonomous Systems: A Quantum Cognitive Approach
12.1 Introduction
12.2 Compatible and Incompatible States
12.3 A Quantum Cognition Model for the Emergence of Trust
12.4 Conclusion
References
13 Learning to Shape Errors with a Confusion Objective
13.1 Introduction
13.2 Foundations
13.2.1 Binomial Logistic Regression
13.2.2 Multinomial Logistic Regression
13.2.3 Multinomial Softmax Regression for Gaussian Case
13.3 Multinomial Softmax Regression on Confusion
13.4 Implementation and Results
13.4.1 Error Trading
13.4.2 Performance Using a Deep Network and Independent Data Sources
13.4.3 Adversarial Errors
13.5 Discussion
13.6 Conclusion
References
14 Developing Robot Assistants with Communicative Cues for Safe, Fluent HRI
14.1 Introduction
14.2 CHARM - Collaborative Human-Focused Assistive Robotics for Manufacturing.
14.2.1 The Robot Assistant, Its Task, and Its Components
14.2.2 CHARM Streams and Thrusts
14.2.3 Plugfest
14.3 Identifying, Modeling, and Implementing Naturalistic Communicative Cues
14.3.1 Phase 1: Human-Human Studies
14.3.2 Phase 2: Behavioral Description
14.3.3 Phase 3: Human-Robot Interaction Studies
14.4 Communicative Cue Studies
14.4.1 Human-Robot Handovers
14.4.2 Hesitation
14.4.3 Tap and Push
14.5 Current and Future Work
References
Trusted Autonomy
15 Intrinsic Motivation for Truly Autonomous Agents
15.1 Introduction
15.2 Background
15.2.1 Previous Work on Intrinsic Human Motivation
15.2.2 Previous Work on Cognitive Architectures
15.3 A Cognitive Architecture with Intrinsic Motivation
15.3.1 Overview of Clarion
15.3.2 The Action-Centered Subsystem
15.3.3 The Non-Action-Centered Subsystem
15.3.4 The Motivational Subsystem
15.3.5 The Metacognitive Subsystem
15.4 Some Examples of Simulations
15.5 Concluding Remarks
References
16 Computational Motivation, Autonomy and Trustworthiness: Can We Have It All?
16.1 Autonomous Systems
16.2 Intrinsically Motivated Swarms
16.2.1 Crowds of Motivated Agents
16.2.2 Motivated Particle Swarm Optimization for Adaptive Task Allocation
16.2.3 Motivated Guaranteed Convergence Particle Swarm Optimization for Exploration and Task Allocation Under Communication Constraints
16.3 Functional Implications of Intrinsically Motivated Swarms
16.3.1 Motivation and Diversity
16.3.2 Motivation and Adaptation
16.3.3 Motivation and Exploration
16.4 Implications of Motivation on Trust
16.4.1 Implications for Reliability
16.5 Implications for Privacy and Security
16.5.1 Implications for Safety
16.6 Implications of Complexity
16.7 Implications for Risk
16.7.1 Implications for Free Will.
16.8 Conclusion
References
17 Are Autonomous-and-Creative Machines Intrinsically Untrustworthy?
17.1 Introduction
17.2 The Distressing Principle, Intuitively Put
17.3 The Distressing Principle, More Formally Put
17.3.1 The Ideal-Observer Point of View
17.3.2 Theory-of-Mind-Creativity
17.3.3 Autonomy
17.3.4 The Deontic Cognitive Event Calculus (mathcalDemathcalCEC)
17.3.5 Collaborative Situations
Untrustworthiness
17.3.6 Theorem ACU
17.4 Computational Simulations
17.4.1 ShadowProver
17.4.2 The Simulation Proper
17.5 Toward the Needed Engineering
References
18 Trusted Autonomous Command and Control
18.1 Scenario
References
19 Trusted Autonomy in Training: A Future Scenario
19.1 Introduction
19.2 Scan of Changes
19.3 Trusted Autonomy Training System Map
19.4 Theory of Change
19.5 Narratives
19.5.1 The Failed Promise
19.5.2 Fake It Until You Break It
19.5.3 To Infinity, and Beyond!
References
20 Future Trusted Autonomous Space Scenarios
20.1 Introduction
20.2 The Space Environment
20.3 Space Activity - Missions and Autonomy
20.4 Current State-of-the-Art of Trusted Autonomous Space Systems
20.5 Some Future Trusted Autonomous Space Scenarios
20.5.1 Autonomous Space Operations
20.5.2 Autonomous Space Traffic Management Systems
20.5.3 Autonomous Disaggregated Space Systems
References
21 An Autonomy Interrogative
21.1 Introduction
21.2 Fundamental Uncertainty in Economics
21.2.1 Economic Agency and Autonomy
21.3 The Inadequacy of Bayesianism
21.4 Epistemic and Ontological Uncertainty
21.5 Black Swans and Universal Causality
21.6 Ontological Uncertainty and Incompleteness
21.6.1 Uncertainty as Non-ergodicity
21.7 Uncertainty and Incompleteness
21.8 Decision-Making Under Uncertainty
21.9 Barbell Strategies.
21.10 Theory of Self.

Foundations of Trusted Autonomy.

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