Interdependent Human-Machine Teams
The Path to Autonomy
- 1st Edition - December 10, 2024
- Editors: William Lawless, Ranjeev Mittu, Donald Sofge, Hesham Fouad
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 2 4 6 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 2 4 7 - 7
Interdependent Human-Machine Teams: The Path to Autonomy examines the foundations, metrics, and applications of human-machine systems, the legal ramifications of autonomy,… Read more
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Interdependent Human-Machine Teams: The Path to Autonomy examines the foundations, metrics, and applications of human-machine systems, the legal ramifications of autonomy, trust by the public, and trust by the users and AI systems of their users, integrating concepts from various disciplines such as AI, machine learning, social sciences, quantum mechanics, and systems engineering. In this book, world-class researchers, engineers, ethicists, and social scientists discuss what machines, humans, and systems should discuss with each other, to policymakers, and to the public.
It establishes the meaning and operation of “shared contexts” between humans and machines, policy makers, and the public and explores how human-machine systems affect targeted audiences (researchers, machines, robots, users, regulators, etc.) and society, as well as future ecosystems composed of humans, machines, and systems.
- Investigates how interdependence is the missing ingredient necessary to produce operational autonomous systems
- Integrates concepts from a wide range of disciplines, including applied and theoretical AI, quantum mechanics, social sciences, and systems engineering
- Presents debates, models, and concepts of mutual dependency for autonomous human-machine teams, challenging assumptions across AI, systems engineering, data science, and quantum mechanics
Computer scientists and systems engineers focused on designing and theorizing about the development of autonomous systems;
- Introduction to “autonomous human-machine teams”
- Toward a new foundation for AI
- Human-machine teaming using large language models
- Development of a team cohesion scale for use in human-autonomy team research
- Enabling human-machine symbiosis: Automated establishment of common ground and estimates of the topological structures of Commander’s Intent
- Measuring consequential changes in human-autonomous system interactions
- User affordances to engineer open-world enterprise dynamics
- Truth-O-Meter: Collaborating with LLM in fighting its hallucinations
- Natural versus artificial intelligence: AI insights from the cognitive sciences
- Intention when humans team with AI
- Autonomy: A family resemblance concept? An exploration of human-robot teams
- A theoretical approach to management of limited attentional resources to support the m:N operation in advanced air mobility ecosystem
- Predicting workload of dispatchers supervising autonomous systems
- The generative AI weapon of mass destruction: Evolving disinformation threats, vulnerabilities, and mitigation frameworks
- Ethics for artificial agents
- Self-visualization for the human-machine mind-body problem
- Knowledge, consciousness, and debate: advancing the science of autonomous human-machine teams
- Edition: 1
- Published: December 10, 2024
- Language: English
WL
William Lawless
William Lawless is professor of mathematics and psychology at Paine College, GA. For his PhD topic on group dynamics, he theorized about the causes of tragic mistakes made by large organizations with world-class scientists and engineers. After his PhD in 1992, DOE invited him to join its citizens advisory board (CAB) at DOE’s Savannah River Site (SRS), Aiken, SC. As a founding member, he coauthored numerous recommendations on environmental remediation from radioactive wastes (e.g., the regulated closure in 1997 of the first two high-level radioactive waste tanks in the USA). He is a member of INCOSE, IEEE, AAAI and AAAS. His research today is on autonomous human-machine teams (A-HMT). He is the lead editor of seven published books on artificial intelligence. He was lead organizer of a special issue on “human-machine teams and explainable AI” by AI Magazine (2019). He has authored over 85 articles and book chapters, and over 175 peer-reviewed proceedings. He was the lead organizer of twelve AAAI symposia at Stanford (2020). Since 2018, he has also been serving on the Office of Naval Research's Advisory Boards for the Science of Artificial Intelligence and Command Decision Making.
Affiliations and expertise
Department of Mathematics, Sciences and Technology, and Department of Social Sciences, School of Arts and Sciences, Paine College, Augusta, GA, USARM
Ranjeev Mittu
Ranjeev Mittu is the Branch Head for the Information and Decision Sciences Branch within the Information Technology Division at the U.S. Naval Research Laboratory (NRL). He leads a multidisciplinary group of scientists and engineers conducting research and advanced development in visual analytics, human performance assessment, decision support systems, and enterprise systems. Mr. Mittu’s research expertise is in multi-agent systems, human-systems integration, artificial intelligence (AI), machine learning, data mining and pattern recognition; and he has authored and/or coedited eleven books on the topic of AI in collaboration with the national and international scientific communities spanning academia and defense. Mr. Mittu received a Master of Science Degree in Electrical Engineering in 1995 from The Johns Hopkins University in Baltimore, MD.
The views expressed in this Work do not necessarily represent the views of the Department of the Navy, the Department of Defense, or the United States.
Affiliations and expertise
Information and Decision Sciences Branch, US Naval Research Laboratory (NRL), Washington, DC, USADS
Donald Sofge
Don Sofge is a computer scientist and roboticist at the Naval Research Laboratory (NRL) with 36 years of experience in artificial intelligence, machine learning, and control systems R&D, the last 23 years at NRL. He leads the Distributed Autonomous Systems Section in the Navy Center for Applied Research in Artificial Intelligence (NCARAI), where he develops nature-inspired computing paradigms to challenging problems in sensing, artificial intelligence, and control of autonomous robotic systems. He has more than 200 refereed publications including 12 edited books in robotics, artificial intelligence, machine learning, planning, sensing, control, and related disciplines.
The views expressed in this Work do not necessarily represent the views of the Department of the Navy, the Department of Defense, or the United States.
Affiliations and expertise
Navy Center for Applied Research in Artificial Intelligence, US Naval Research Laboratory (NRL), Washington, DC, USAHF
Hesham Fouad
Hesham Fouad is the section head for the Intelligent Decision Support Section within the Information Technology Division at the U.S. Naval Research Laboratory (NRL). Dr. Fouad has over 35 years of experience as a Computer Scientist working in both industry and academia. He began his career at IBM Advanced Technologies where he worked on the first commercially available expert system development and runtime environment. Expert System Environment (ESE) was developed through a collaborative effort between the AI team at Stanford University and IBM. Dr. Fouad worked with the Stanford team to integrate new capabilities into ESE such as Frame Based Reasoning. Dr. Fouad also worked with the Voice Recognition group, led by Kai-Fu Lee, at Carnegie Mellon university to transition their S&T into IBM’s Via Voice product. Finally, Dr. Fouad worked with the MIT media lab’s Project Athena group on a collaborative effort to integrate a Hypermedia capability into the OS/2 operating system.
Dr. Fouad left IBM to pursue a Doctoral Degree in Computer Science at The George Washington University where he conducted his dissertation research on optimal real-time scheduling algorithms for Imprecise Computations using a fair scheduling strategy. This work was the basis of a startup company that Dr. Fouad founded and ran for 13 years. During that time, Dr. Fouad developed and managed the production of a line of commercial software for synthetic spatial audio design and production. He also conducted research on, and managed several ONR funded BAA and SBIR awards on adaptive training in virtual environments during that time.
Dr. Fouad maintained his involvement with academia both as an adjunct professor at the George Washington University, and as chair of a newly created undergraduate degree program in Computer Science with a focus on Game Development at the Art Institute of Washington. Since joining NRL, Dr. Fouad has led numerous efforts with three transitions to-date. He has procured millions in funding from Navy, Marines, Army, and OUSD sources. He has mentored SEAP students and has taken on a variety of roles within The Technical Cooperation Program.
Affiliations and expertise
Information Management and Decision Architectures Branch, Information Technology Division, U.S. Naval Research Laboratory, Washington, DC, USARead Interdependent Human-Machine Teams on ScienceDirect