Systems engineering and artificial intelligence
This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validat...
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| Main Authors | , , , , |
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| Format | eBook Book |
| Language | English |
| Published |
Cham
Springer
2021
Springer International Publishing AG Springer International Publishing |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 3030772829 9783030772826 |
| DOI | 10.1007/978-3-030-77283-3 |
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| Abstract | This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams-where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges. |
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| AbstractList | This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams-where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges. This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. -- |
| Author | Lawless, William F. Sofge, Donald A. McDermott, Thomas A. Shortell, Thomas M. Mittu, Ranjeev |
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| Copyright | Springer Nature Switzerland AG 2021. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply |
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| Editor | Lawless, William F. Sofge, Donald A. McDermott, Thomas A. Shortell, Thomas Mittu, Ranjeev |
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| Notes | Includes bibliographical references Print on demand edition |
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| SubjectTerms | Artificial Intelligence Artificial intelligence -- Industrial applications Artificial intelligence-Mathematical models Computer Science Deformations (Mechanics) Fracture mechanics Special Purpose and Application-Based Systems Systems engineering User Interfaces and Human Computer Interaction |
| SubjectTermsDisplay | Deformations (Mechanics). Electronic books. Fracture mechanics. |
| TableOfContents | 15 Humanity in the Era of Autonomous Human-machine Teams -- 15.1 Introduction: AHMTs in the Form of the Trio -- 15.1.1 The Trio: Data, the Internet, and Algorithms -- 15.1.2 AHMTs Manifested by the Trio -- 15.1.3 Scitovsky's Caveat -- 15.2 Human-Machine Teams -- 15.2.1 Shelley Model: Frankenstein and His Creature -- 15.2.2 Lovelock Model: GAIA and Novacene -- 15.2.3 Margulis Model: Symbiogenesis and Super Cooperators -- 15.2.4 Polanyi Model: Tension Between Habitation and Improvement -- 15.2.5 Laloux Model: Soulful Organizations -- 15.3 Meaning of the Trios for Humanity -- 15.3.1 Co-evolutions of Humans and Machines -- 15.3.2 Individuality -- 15.3.3 Democratization of Individuality -- 15.4 Meaning of the Trio for the Humanities -- 15.4.1 Distant Reading -- 15.4.2 Extended Reading -- 15.4.3 Participatory Reading -- 15.5 Concluding Remarks -- References -- 16 Transforming the System of Military Medical Research: An Institutional History of the Department of Defense's (DoD) First Electronic Institutional Review Board Enterprise IT System -- 16.1 Introduction. A Tale of Two Histories -- 16.1.1 Goal 1: The eIRB Transformed the MEDCENs -- 16.1.2 Goal 2: The Initial Meeting on Collaboration -- 16.1.3 Our Two Goals Merged into One -- 16.2 The Next Steps in the Transformation from a Paper to Electronic System -- 16.3 Boundary Maintenance -- 16.4 Future Steps to Determine Impacts. Preliminary Results in 2010 -- 16.5 Summary -- 16.6 Postscript -- References -- 17 Collaborative Communication and Intelligent Interruption Systems -- 17.1 Introduction -- 17.2 Interruptions in Multi-user Multitasking Interactions -- 17.2.1 Low Cognitive Interruption Timings -- 17.2.2 High Cognitive Interruption Timings -- 17.3 Methods -- 17.3.1 Data Collection -- 17.3.2 Conditions -- 17.4 Results and Discussion -- 17.4.1 Team Performance Analyses 8.7 Design Science: Toward the Science of AI System Engineering -- 8.8 Conclusion -- References -- 9 The Department of Navy's Digital Transformation with the Digital System Architecture, Strangler Patterns, Machine Learning, and Autonomous Human-Machine Teaming -- 9.1 Introduction -- 9.2 Autonomous Human-Machine Teaming Lifecycle Difficulties -- 9.3 Unique Challenges Facing the Department of Navy and Autonomous Human-Machine Teaming -- 9.3.1 Department of Navy Non-technical Challenges -- 9.3.2 Department of Navy Technical Challenges -- 9.4 Attacking the Technical Debt and Inflation to Enable AHMT Solutions -- 9.4.1 AHMT Solutions and New Target Platforms -- 9.4.2 AHMT Solutions and Legacy Target Platforms -- 9.5 Conclusion and Path Forward -- References -- 10 Digital Twin Industrial Immune System: AI-driven Cybersecurity for Critical Infrastructures -- 10.1 Introduction -- 10.1.1 Overview -- 10.1.2 Cybersecurity Technology Gaps for Advanced Detection, Protection and Monitoring Solutions -- 10.1.3 Digital Ghost: A Next-Generation Response to Close Critical Energy Infrastructure Gaps -- 10.2 People, Process and Technology Applicability Gap Analysis -- 10.2.1 Attack Detection -- 10.2.2 Attack Localization -- 10.2.3 Attack Neutralization -- 10.2.4 Man Versus Machine Anomaly Forecasting and Detection -- 10.3 Digital Ghost Research Findings and Future Research -- 10.3.1 Invariant Learning -- 10.3.2 Autonomous Defense: Critical Sensors Identification and Trust -- 10.3.3 Humble AI -- 10.3.4 Explainable AI (XAI) -- 10.4 Conclusion -- References -- 11 A Fractional Brownian Motion Approach to Psychological and Team Diffusion Problems -- 11.1 Introduction -- 11.2 Random Walk -- 11.2.1 Wiener Process from the Fair Simple Random Walk -- 11.2.2 Wiener Process (standard Brownian Motion) Defined -- 11.2.3 Simulation of the Wiener Process via G0,1n 5.6 Conclusions and Future Directions -- References -- 6 Systems Engineering for Artificial Intelligence-based Systems: A Review in Time -- 6.1 Perspectives on AI and Systems Engineering -- 6.2 The Dynamics of This Space -- 6.2.1 Evolving an SE Framework: Ontologies of AI/ML-Dealing with the Breadth of the Fields -- 6.2.2 Systems Engineering as a Moving Target -- 6.2.3 The First to Market Motivation -- 6.2.4 Technical Debt -- 6.2.5 Summary -- 6.3 Stepping Through Some Systems Engineering Issues -- 6.3.1 Capability Maturity Model Integration [CMMI] and SE for R& -- D -- 6.3.2 Requirements Engineering -- 6.3.3 Software Engineering for AI/ML Systems -- 6.3.4 Test and Evaluation -- 6.4 Sampling of Technical Issues and Challenges -- 6.4.1 Emergence and Emergent Behavior -- 6.4.2 Safety in AI/ML -- 6.4.3 The Issue of Explanation/Explainability -- 6.5 Summary -- References -- 7 Human-Autonomy Teaming for the Tactical Edge: The Importance of Humans in Artificial Intelligence Research and Development -- 7.1 Introduction -- 7.2 The Fundamental Nature of Human-Autonomy Teaming -- 7.2.1 Complementarity of Human and AI Characteristics -- 7.2.2 Tracking the Important Roles of the Human Across AI History -- 7.3 Artificial Intelligence for Human-Autonomy Teams -- 7.3.1 Quantifying Soldier Understanding for AI -- 7.3.2 Soldier-Guided AI Adaptations -- 7.3.3 Characterizing Soldier-Autonomy Performance -- 7.4 Conclusions -- References -- 8 Re-orienting Toward the Science of the Artificial: Engineering AI Systems -- 8.1 Introduction -- 8.2 AI Software Engineering -- 8.3 AI-enabled Complex Systems-of-Systems and Emergent Behaviors -- 8.4 The Importance of Interoperability -- 8.5 The Role of Uncertainty in ML -- 8.6 The Challenge of Data and ML: An NLP Example -- 8.6.1 System Architecture -- 8.6.2 Results -- 8.6.3 Discussion 17.4.2 Individual Subjective Analyses Intro -- Preface -- Contents -- 1 Introduction to "Systems Engineering and Artificial Intelligence" and the Chapters -- 1.1 Introduction. The Disruptive Nature of AI -- 1.1.1 Justifying Speedy Decisions -- 1.1.2 Systems Engineering (SE) -- 1.1.3 Common Ground: AI, Interdependence, and SE -- 1.1.4 Social Science -- 1.1.5 The Science of Human Teams -- 1.1.6 Human-Machine Teams -- 1.2 Introduction to the Chapters -- 1.3 Summary -- References -- 2 Recognizing Artificial Intelligence: The Key to Unlocking Human AI Teams -- 2.1 Introduction -- 2.1.1 Motivation and Goals -- 2.1.2 Types of Human-AI Collaboration -- 2.1.3 Ground Rules -- 2.2 System Engineering -- 2.2.1 Design and Embodiment -- 2.2.2 Generative Language Models -- 2.2.3 System Architecture -- 2.2.4 Agile Development -- 2.3 Applications -- 2.3.1 Ideation Discussions -- 2.3.2 Collaborative Writing -- 2.4 Innovative Brainstorm Workshop -- 2.4.1 Protocol -- 2.4.2 Analysis -- 2.4.3 Preliminary Results -- 2.5 Related Work -- 2.6 Future Applications -- 2.7 Conclusion -- References -- 3 Artificial Intelligence and Future of Systems Engineering -- 3.1 Introduction -- 3.2 SERC AI4SE and SE4AI Roadmap -- 3.3 Digital Engineering -- 3.4 AI/ML Technology Evolution -- 3.5 Augmented Engineering -- 3.6 Workforce and Culture -- 3.7 Summary-The AI imperative for Systems Engineering -- References -- 4 Effective Human-Artificial Intelligence Teaming -- 4.1 Introduction -- 4.2 Synthetic Teammates -- 4.3 HAT Findings and Their Implications for Human Teams -- 4.4 Conclusions and Future Work -- References -- 5 Toward System Theoretical Foundations for Human-Autonomy Teams -- 5.1 Introduction -- 5.2 Organizational Structure and Role/Function Allocation -- 5.3 Working Together on Tasks -- 5.4 Teaming Over Longer Durations -- 5.5 Formally Modeling and Composing Complex Human-Machine Systems 11.2.4 Continuity of Sample Paths -- 11.2.5 Non-differentiability of Wiener Process Sample Paths -- 11.3 Brownian Motion -- 11.3.1 Simulation of Brownian Motion -- 11.4 Stopping Times and Absorbing Boundaries -- 11.4.1 Two Absorbing Boundaries-The Situation for Ratcliff Drift Diffusion -- 11.5 Fractional Brownian Motion -- 11.5.1 Covariance of Brownian Motion -- 11.5.2 Definition of the Fractional Wiener Process -- 11.5.3 Existence and Properties of the Fractional Wiener Process -- 11.5.4 Ratcliff Diffusion Revisited -- 11.6 Determining H, a Problem in AI -- 11.6.1 Our Hybrid Approach -- 11.7 Team Science and Future Work -- References -- 12 Human-Machine Understanding: The Utility of Causal Models and Counterfactuals -- 12.1 Introduction -- 12.2 Information-Theoretic Framework for SCM Construction -- 12.3 Assessing and Correcting for Bias in Information-Theoretic SCM Construction -- 12.4 Construction of SCM for Counterfactuals -- 12.5 Notes on Related Work -- 12.6 Summary -- References -- 13 An Executive for Autonomous Systems, Inspired by Fear Memory Extinction -- 13.1 The Problem -- 13.2 Moondoodya, a Novel Electronic Warfare System -- 13.3 PTSD Fear Extinction -- 13.4 A Mathematical Approach to Executive Abstraction -- 13.5 'Effect First' Modelling -- 13.6 A Closure Embedding Strategy -- 13.7 The Tookoonooka Vortex Collaborative -- 13.8 Conclusions -- References -- 14 Contextual Evaluation of Human-Machine Team Effectiveness -- 14.1 Introduction -- 14.2 Related Works -- 14.3 Background -- 14.3.1 Interference -- 14.3.2 Inverse Reinforcement Learning (IRL) -- 14.3.3 Preferential Trajectory-Based IRL (PT-IRL) -- 14.4 Approach -- 14.4.1 Experimental Setup -- 14.4.2 Training Classifier -- 14.4.3 Human and Human-Machine Teams -- 14.4.4 Evaluation of Human-Machine Team Effectiveness -- 14.5 Conclusion and Future Work -- References |
| Title | Systems engineering and artificial intelligence |
| URI | https://cir.nii.ac.jp/crid/1130572155855361685 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=6797258 http://link.springer.com/10.1007/978-3-030-77283-3 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9783030772833 |
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