Bi-Directionality in Human-AI Collaborative Systems

Erscheinungsjahr 2025
ISBN: 978-0-443-40553-2
Verlag: Elsevier Science

Buch, Englisch, 300 Seiten, Format (B × H): 191 mm x 235 mm

Bi-directionality in Human-AI Collaborative Systems investigates the foundations, metrics, and applications of human-machine systems, along with the legal ramifications of autonomy, including standards, trust by the public, and bidirectional trust by users and AI systems. The book addresses the challenges in creating synergistic human and AI-based autonomous system-of-systems by focusing on the underlying challenges associated with bi-directionality. Chapters cover advances in LLMs, logic, machine learning choices, the development of standards, as well as human-centered approaches to autonomous human-machine teams. This is a valuable resource for world-class researchers and engineers who are theorizing on, designing, and developing autonomous systems.

It will also be useful for government scientists, business leaders, social scientists, philosophers, regulators and legal experts interested in the impact of autonomous human-machine teams and systems.

Autoren/Hrsg.

Lawless, William

Mittu, Ranjeev

Sofge, Donald

Brambilla, Marco

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion

Inhaltsverzeichnis

1. Introduction
2. Interdependence in the human-machine fusion process
3. Advances in large language models
4. Logic applied to machines as part of a human-machine team
5. Machine learning model choices
6. Mixing machines and humans with mathematics
7. The development of standards for human-machine teams
8. The Systems Engineering Research Center’s approach to teams of swarms, machines and humans
9. Human-machine teams in aviation
10. Autonomous human-machine teams in Australia
11. A human-centered approach to autonomous human-machine teams
12. Risks and ethics in human-machine teams
13. Data Poisoning in human-machine teams
14. Trust and among human-machine teammates
15. Belief and consciousness in human-machine teams
16. Explainability in human-machine teams
17. Risk, trust, and safety in human-machine teams
18. Joint awareness in human-machine teams 19. Shared mental models in human-machine teams
20. System design and engineering for human-machine teams
21. Testing and evaluation of human-machine teams

Über Autor(innen)

Brambilla, Marco
Marco Brambilla is full professor at Politecnico di Milano. He is active in research and innovation, both at industrial and academic level. His research interests include data science, software modeling languages and design patterns, crowdsourcing, social media monitoring, and big data analysis. He has been visiting researcher at CISCO, San Josè, and University of California, San Diego. He has been visiting professor at Dauphine University, Paris. He is founder of various startups and spinoffs, including WebRatio, Fluxedo, and Quantia, focusing on social media analysis, software modeling, Mobile and Business Process based software applications, and data science projects. He is author of various international books including Model Driven Software Development in Practice (II edizione, Morgan-Claypool, 2017, adopted in 100+ universities worldwide), Web Information Retrieval (Springer, 2013), Interaction Flow Modeling Language (Morgan-Kauffman, 2014), Designing Data-Intensive Web Applications (Morgan-Kauffman, 2002). He also authored more than 250 research articles in top research journals and conferences. He was awarded various best paper awards and gave keynotes and speeches at many conferences and organisations. He is the main author of the OMG (Object Management Group) standard IFML (Interaction Flow Modeling Language). He participated in several European and international research projects. He has been reviewer of FP7 projects and evaluator of EU FP7 proposals, as well as of national and local government funding programmes throughout Europe. He has been PC chair of ICWE 2008 and ICWE 2021, as well as co-chair of various tracks, conferences and workshops. He is associate editor of various journals and PC member of several conferences and workshops.

Mittu, Ranjeev
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.

Sofge, Donald
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.