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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
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
- No. of pages:
- © Academic Press 2020
- 1st June 2020
- Academic Press
- Paperback ISBN:
Dr. Lawless was formerly an engineer who oversaw nuclear waste management; in 1983, he became a whistle blower against the Department of Energy’s (DOE) mismanagement of its radioactive wastes. For his PhD, he studied the causes of mistakes by large organizations with world-class scientists and engineers. After his PhD, DOE invited him to join its citizen advisory board at DOE’s Savannah River Site, Aiken, SC. As a founding member, he coauthored over 100 recommendations on DOE’s environmental remediation. Afterwards, he became the Board’s technical advisor. He was a member of the European Trustnet hazardous decisions group. He is a senior member of IEEE. His research is on mathematical metrics for teams. He has previously published three co-edited books on Artificial Intelligence. He has published over 70 articles and book chapters, 145 peer-reviewed proceedings; and is co-editing a Special Issue of AI Magazine on “Computational context”. He has co-organized eight AAAI symposia at Stanford (e.g., in March 2018: Artificial Intelligence for the Internet of Everything; in March 2019: Artificial intelligence (AI), autonomous machines and constructing context: User interventions, social awareness and interdependence; see at: http://www.aaai.org/Symposia/Spring/sss19.php; see our supplemental website for details: https://sites.google.com/site/aaai19sharedcontext/).
Department of Mathematics, Sciences and Technology, and Department of Social Sciences, School of Arts and Sciences, Paine College, Augusta, GA, USA
Mr. 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. His research expertise is in multi-agent systems, artificial intelligence, machine learning, data mining, pattern recognition and anomaly detection. He has a track record for transitioning technology solutions to the operational community. Mr. Mittu received a technology transfer award at NRL in August 2012 for transitioning software to USTRANSCOM. He has authored one book, coedited three books, and written 5 book chapters and numerous conference publications. He has an MS in Electrical Engineering from The Johns Hopkins University. He has participated in The Technical Cooperation Program (TTCP) for scientific exchanges between New Zealand, UK, Australia, Canada and USA (2005-09); served as Subject Matter Expert for Joint IED Defeat Organization (2007-2008) and Netcentric Systems Test S&T working group in association with U.S. Army Program Executive Office for Simulation, Training, and Instrumentation (PEO STRI); and served on NRL’s Invention Evaluation Board (IEB) to evaluate technologies and concepts for potential filing with the USPTO (2006-2008).
Information Management and Decision Architectures Branch, Information Technology Division, U.S. Naval Research Laboratory, Washington, DC, USA
Mr. Sofge is a Computer Scientist and Roboticist at the U.S. Naval Research Laboratory (NRL) with 30 years of experience in Artificial Intelligence and Control Systems R&D. He has served as PI or Co-PI on dozens of federally funded R&D programs, and has more than 100 peer-reviewed publications on autonomy, intelligent control, quantum computing, and related topics, including 5 books, 10 book chapters, 19 journal articles, 62 conference papers, and one patent. He leads the Distributed Autonomous Systems Group at NRL where he develops nature-inspired computing solutions to challenging problems in sensing, artificial intelligence, and the control of autonomous robotic systems. His current research focuses on the control of autonomous teams or swarms of robotic systems for Navy relevant missions. He has served on numerous technical peer review panels for government agencies including ONR, DARPA, Army, NASA, and NSF. He is also frequently called upon to review submissions for a variety of journals and conferences, and regularly serves in a professional capacity to help organize conferences, symposia, and workshops. He currently serves on several editorial boards for journals related to computational intelligence and control, as well as technical advisory panels on robotics and autonomy (viz., OSTP, OSD, DARPA, NSF, TTCP, and NATO). He also serves as Adjunct Faculty at the University of Maryland where he teaches a popular graduate-level course on "Robot Learning".
Navy Center for Applied Research in Artificial Intelligence, United States Naval Research Laboratory, Washington, DC, USA