TUM Library

Includes bibliographical references and index.

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.

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.

Also available in print format.