Human-Machine Shared Contexts considers the foundations, metrics, and applications of human-machine systems. Editors and authors debate whether machines, humans, and systems should speak only to each other, only to humans, or to both and how. The book establishes the meaning and operation of “shared contexts” between humans and machines; it also explores how human-machine systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems composed of humans and machines. This book explores how user interventions may improve the context for autonomous machines operating in unfamiliar environments or when experiencing unanticipated events; how autonomous machines can be taught to explain contexts by reasoning, inferences, or causality, and decisions to humans relying on intuition; and for mutual context, how these machines may interdependently affect human awareness, teams and society, and how these "machines" may be affected in turn. In short, can context be mutually constructed and shared between machines and humans? The editors are interested in whether shared context follows when machines begin to think, or, like humans, develop subjective states that allow them to monitor and report on their interpretations of reality, forcing scientists to rethink the general model of human social behavior. If dependence on machine learning continues or grows, the public will also be interested in what happens to context shared by users, teams of humans and machines, or society when these machines malfunction. As scientists and engineers "think through this change in human terms," the ultimate goal is for AI to advance the performance of autonomous machines and teams of humans and machines for the betterment of society wherever these machines interact with humans or other machines. This book will be essential reading for professional, industrial, and military computer scientists and engineers; machine learning (ML) and artificial intelligence (AI) scientists and engineers, especially those engaged in research on autonomy, computational context, and human-machine shared contexts; advanced robotics scientists and engineers; scientists working with or interested in data issues for autonomous systems such as with the use of scarce data for training and operations with and without user interventions; social psychologists, scientists and physical research scientists pursuing models of shared context; modelers of the internet of things (IOT); systems of systems scientists and engineers and economists; scientists and engineers working with agent-based models (ABMs); policy specialists concerned with the impact of AI and ML on society and civilization; network scientists and engineers; applied mathematicians (e.g., holon theory, information theory); computational linguists; and blockchain scientists and engineers.
Discusses the foundations, metrics, and applications of human-machine systems
Considers advances and challenges in the performance of autonomous machines and teams of humans
Debates theoretical human-machine ecosystem models and what happens when machines malfunction
Graduate students, researchers, professionals in computer science, human factors engineering, robotics engineering, artificial intelligence, and machine intelligence
Table of Contents
1. Introduction 2. Interaction, Analogy, and Meta reasoning: Applying Cognitive Science to Robotics 3. Context: Adding Command Knowledge ‘At the Human Edge’ 4. Separating the Forest and The Trees - Wavelet Contextual Conditioning For AI 5. A Narrative Modeling Platform: Representing the Comprehension of Novelty in Open World Systems 6. Deciding Machines: Moral-Scene Assessment for Intelligent Systems 7. The Criticality of Social & Behavioral Science in the Development and Execution of Autonomous Systems 8. Virtual Health and Artificial Intelligence: Using Technology to Improve Healthcare Delivery 9. An Information Geometric look at the Valuing of Information 10. AI, Autonomous Machines and Human Awareness: Towards Shared Machine-Human Contexts in Medicine 11. Problems of Autonomous Agents following Informal, Open-textured Rules 12. Engineering for Emergence in Information Fusion Systems: A Review of Some Challenges 13. Integrating Expert Human Decision-Making in Artificial Intelligence Applications 14. A Communication Paradigm for Human-Robot Interaction During Robot Failure Scenarios 15. On Neural-Network Training Algorithms 16. Identifying Distributed Incompetence in an Organization 17. Begin with the human: Designing for safety and trustworthiness in cyber-physical systems 18. Digital Humanities and Digital Economy 19. Human-Machine Sense Making in Context Based Computational Decisions 20. Constructing Mutual Context in Human-robot Collaborative Problem-solving with Multi-modal Input
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, USA
Ranjeev Mittu is the branch head for the Information Management and Decision Architectures Branch within the Information Technology Division at the U.S. Naval Research Laboratory (NRL). He leads a multidisciplinary group of scientists and engineers that conduct 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 nine books on the topic of AI in collaboration with 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.
Affiliations and Expertise
Information Management and Decision Architectures Branch, Information Technology Division, U.S. Naval Research Laboratory, Washington, DC, USA
Don Sofge is a computer scientist and roboticist at the Naval Research Laboratory (NRL) with 33 years of experience in artificial intelligence, machine learning, and control systems R&D. He leads the Distributed Autonomous Systems Group 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 180 refereed publications including 10 books in robotics, artificial intelligence, machine learning, planning, sensing, control, and related disciplines.
Affiliations and Expertise
Navy Center for Applied Research in Artificial Intelligence, United States Naval Research Laboratory, Washington, DC, USA