Computational Context

Auteur
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) Sofgeis a Computer Scientist andRoboticist at the U.S. Naval Research Laboratory (NRL) with 30 years ofexperience in Artificial Intelligence andControl Systems R&D. He hasserved as PI/Co-PI on dozens of federally funded R&D programs and hasauthored/co-authored approximately 110 peer-reviewed publications,includingseveral edited books, many journal articles, and several conferenceproceedings. Don leads the Distributed Autonomous Systems Group at NRL where hedevelopsnature-inspired computing solutions to challenging problems insensing, artificial intelligence, and control of autonomous robotic systems.His current research focuseson control of autonomous teams or swarms of roboticsystems.

Résumé
Autonomous systems can manage uncertainty better than humans, but autonomous systems can also fail. Perturbations against a team may clarify context; e.g., a competition between teams. But modeling perturbations, especially between multiple autonomous hybrid human-machine-robot systems, is a challenges address in this book.

Contenu

Introduction. Learning Context through Cognitive Priming. The Use of Contextual Knowledge in a Digital Society. Challenges with addressing the issue of context within AI and human-robot teaming. Machine Learning Approach for Task Generation in Uncertain Contexts. Creating and Maintaining a World Model for Automated Decision Making. Probabilistic Scene Parsing. Using Computational Context Models to Generate Robot Adaptive Interactions with Humans. Context-Driven Proactive Decision Support: Challenges and Applications. The Shared Story Narrative Principles for Innovative Collaboration. Algebraic Modeling of the Causal Break and Representation of the Decision Process in Contextual Structures. A Contextual Decision-Making Framework. Cyber-(in)Security, context and theory: Proactive Cyber-Defenses.