Foundations of Trusted Autonomy.

• Superior document: Studies in Systems, Decision and Control Series ; v.117
• Author: Abbass, Hussein A.
• TeilnehmendeR: Scholz, Jason.; Reid, Darryn J.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2018.; Ã2018.
• Year of Publication: 2018
• Edition: 1st ed.
• Language: English
• Series: Studies in Systems, Decision and Control Series
• Physical Description: 1 online resource (399 pages)

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.