Artificial Intelligence for the Internet of Everything. Este artículo no está disponible.Artificial Intelligence for the Internet of Everything

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

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.

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.

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.

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 ®.

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.