Artificial Intelligence for the Internet of Everything considers the foundations, metrics and applications of IoE systems. It covers whether devices and IoE systems should speak only to each other, to humans or to both. Further, the book explores how IoE systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems. It examines the meaning, value and effect that IoT has had and may have on ordinary life, in business, on the battlefield, and with the rise of intelligent and autonomous systems. Based on an artificial intelligence (AI) perspective, this book addresses how IoE affects sensing, perception, cognition and behavior.
Each chapter addresses practical, measurement, theoretical and research questions about how these “things” may affect individuals, teams, society or each other. Of particular focus is what may happen when these “things” begin to reason, communicate and act autonomously on their own, whether independently or interdependently with other “things”.
- Considers the foundations, metrics and applications of IoE systems
- Debates whether IoE systems should speak to humans and each other
- Explores how IoE systems affect targeted audiences and society
- Discusses theoretical IoT ecosystem models
Graduate students, researchers, academics and professionals in the areas of engineering, human factors, robotics, applied psychology, defense, computer science, and machine intelligence
2. Uncertainty Quantification in Internet of Battlefield Things
3. Intelligent Autonomous Things on the Battlefield
4. Active Inference in Multi-agent Systems: Context-driven Collaboration and Decentralized Purpose-driven Team Adaptation
5. Policy Issues Regarding Implementations of Cyber Attack. Resilience Solutions for Cyber Physical Systems
6. Trust and Human-Machine Teaming: A Qualitative Study
7. The Web of Smart Entities – Aspects of a Theory of the Next Generation of the Internet of Things
8. Raising Them Right: AI and the Internet of Big Things
9. Valuable Information and the Internet of Things
10. Would IOET Make Economics More Neoclassical or More Behavioral? Richard Thaler’s Prediction, A Revisit
11. Accessing Validity of Argumentation of Agents of the Internet of Everything
12. Distributed Autonomous Energy Organizations: Next Generation Blockchain Applications for Energy Infrastructure
13. Compositional Models for Complex Systems
14. Meta-agents: Using Multi-Agent Networks to Manage Dynamic Changes in the Internet of Things (IoT)
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- © Academic Press 2019
- 25th February 2019
- Academic Press
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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 & 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
Dr. Moskowitz has been a mathematician at the Naval Research Laboratory (NRL) for 29 years; presently, he is in the Information Management and Decision Architectures Branch within NRL’s Information Technology Division. Prior to his work at NRL, he was a mathematics professor. His PhD was in Differential Topology from Stony Brook University, Stony Brook, New York. His research areas are information theory and information hiding. His major contributions have been to the area of covert channel analysis. He has over 120 publications and three patents. In particular he is the co-inventor of the NRL Network Pump ®.
United States Naval Research Laboratory, DC, USA
Dr. Russell is currently the Battlefield Information Processing Branch Chief at the Army Research Laboratory. Dr. Russell received a B.Sc. in Computer Science and M.S. and Ph.D. degrees in Information Systems from the University of Maryland. His primary research interests are in the area of decision support systems, machine learning, systems architectures, and intelligent systems. His published research articles appear in Expert Systems with Applications, Decision Support Systems Journal, the Encyclopedia of Decision Making and Decision Support Technologies, and Frontiers in Bioscience, amongst others.
Battlefield Information Processing Branch, United States Army Research Laboratory, Adelphi, MD, USA