This volume addresses context from three comprehensive perspectives: first, its importance, the issues surrounding context, and its value in the laboratory and the field; second, the theory guiding the AI used to model its context; and third, its applications in the field (e.g., decision-making). This breadth poses a challenge. The book analyzes how the environment (context) influences human perception, cognition and action. While current books approach context narrowly, the major contribution of this book is to provide an in-depth review over a broad range of topics for a computational context no matter its breadth. The volume outlines numerous strategies and techniques from world-class scientists who have adapted their research to solve different problems with AI, in difficult environments and complex domains to address the many computational challenges posed by context.
Context can be clear, uncertain or an illusion. Clear contexts: A father praising his child; a trip to the post office to buy stamps; a policewoman asking for identification. Uncertain contexts: A sneak attack; a surprise witness in a courtroom; a shout of Fire! Fire! Contexts as illusion: Humans fall prey to illusions that machines do not (Adelson's checkerboard illusion versus a photometer). Determining context is not easy when disagreement exists, interpretations vary, or uncertainty reigns. Physicists like Einstein (relativity), Bekenstein (holographs) and Rovelli (universe) have written that reality is not what we commonly believe. Even outside of awareness, individuals act differently whether alone or in teams.
Can computational context with AI adapt to clear and uncertain contexts, to change over time, and to individuals, machines or robots as well as to teams? If a program automatically knows the context that improves performance or decisions, does it matter whether context is clear, uncertain or illusory? Written and edited by world class leaders from across the field of autonomous systems research, this volume carefully considers the computational systems being constructed to determine context for individual agents or teams, the challenges they face, and the advances they expect for the science of context.
TABLE OF CONTENTS Introduction W.F. Lawless, Ranjeev Mittu, and Donald Sofge Learning Context through Cognitive Priming Laura M. Hiatt, Wallace E. Lawson, and Mark Roberts The Use of Contextual Knowledge in a Digital Society Shu-Heng Chen, and Ragupathy Venkatachalam Challenges with addressing the issue of context within AI and human-robot teaming Kristin E Schaefer, Derya Aksaray, Julia Wright, and Nicholas Roy Machine Learning Approach for Task Generation in Uncertain Contexts Luke Marsh, Iryna Dzieciuch, and Douglas S. Lange Creating and Maintaining a World Model for Automated Decision Making Hope Allen, and Donald Steiner Probabilistic Scene Parsing Michael Walton, Doug Lange, and Song-Chun Zhu Using Computational Context Models to Generate Robot Adaptive Interactions with Humans Wayne Zachary, Taylor J Carpenter, and Thomas Santarelli Context-Driven Proactive Decision Support: Challenges and Applications Manisha Mishra, David Sidoti, Gopi V. Avvari, Pujitha Mannaru, Diego F. M. Ayala, and Krishna R. Pattipati The Shared Story - Narrative Principles for Innovative Collaboration Beth Cardier Algebraic Modeling of the Causal Break and Representation of the Decision Process in Contextual Structures Olivier Bartheye and Laurent Chaudron A Contextual Decision-Making Framework Eugene Santos Jr., Hien Nguyen, Keum Joo Kim, Jacob A. Russell, Gregory M. Hyde, Luke J. Veenhuis, Ramnjit S. Boparai, Luke T. De Guelle, and Hung Vu Mac Cyber-(in)Security, context and theory: Proactive Cyber-Defenses Lawless, W.F., Mittu, R., Moskowitz, I.S., Sofge, D.A. and Russell, S.
William Lawless, as an engineer, in 1983, Lawless blew the whistle on Department of Energy's mismanagement of radioactive wastes. For his PhD, he studied the causes of mistakes by organizations with world-class scientists and engineers. Afterwards, DOE invited him onto its citizen advisory board at its Savannah River Site where he co-authored numerous recommendations on the site's clean-up. In his research on mathematical metrics for teams, he has published two co-edited books on AI, and over 200 articles, book chapters and peer-reviewed proceedings. He has co-organized eight AAAI symposia at Stanford (e.g., in 2018: Artificial Intelligence for the Internet of Everything). Ranjeev Mittu, is a Branch Head for the Information Management and Decision Architectures Branch within the Information Technology Division at the U.S. Naval Research Laboratory. He is the Section Head of Intelligent Decision Support Section which develops novel decision support systems through applying technologies from the AI, multi-agent systems and web services. He brings a strong background in transitioning R&D solutions to the operational community, demonstrated through his current sponsors including DARPA, OSD/NII, NSA, USTRANSCOM and ONR. He has authored 2 books, 5 book chapters, and numerous conference publications. He has an MS in Electrical Engineering from Johns Hopkins University. Donald (Don) 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/Co-PI on dozens of federally funded R&D programs and has authored/co-authored approximately 110 peer-reviewed publications, including several edited books, many journal articles, and several conference proceedings. Don leads the Distributed Autonomous Systems Group at NRL where he develops nature-inspired computing solutions to challenging problems in sensing, artificial intelligence, and control of autonomous robotic systems. His current research focuses on control of autonomous teams or swarms of robotic systems.