Artificial Intelligence for the Internet of Everything
Published on 25. February 2019
Book
Paperback/Softback
303 pages
978-0-12-817636-8 (ISBN)
Description
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?.
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?.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Graduate students, researchers, academics and professionals in the areas of engineering, human factors, robotics, applied psychology, computer science, and machine intelligence.
Product notice
Paperback (trade)
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 16 mm
Weight
410 gr
ISBN-13
978-0-12-817636-8 (9780128176368)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
William Lawless | Ranjeev Mittu | Donald Sofge
Artificial Intelligence for the Internet of Everything
E-Book
02/2019
Academic Press
€109.00
Available for download
Persons
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 (R). 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.
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 (R). 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.
Editor
Professor, Department of Mathematics, Sciences and Technology, and Department of Social Sciences, School of Arts and Sciences, Paine College, Augusta, GA, USA
Information and Decision Sciences Branch, US Naval Research Laboratory (NRL), Washington, DC, USA
Navy Center for Applied Research in Artificial Intelligence, US Naval Research Laboratory (NRL), Washington, DC, USA
United States Naval Research Laboratory, DC, USA
Battlefield Information Processing Branch, United States Army Research Laboratory, Adelphi, MD, USA
Content
1. Introduction
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)
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)