Human-Machine Shared Contexts

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.

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• 1. Introduction2. Interaction, Analogy, and Meta reasoning: Applying Cognitive Science to Robotics3. Context: Adding Command Knowledge ‘At the Human Edge’4. Separating the Forest and The Trees - Wavelet Contextual Conditioning For AI5. A Narrative Modeling Platform: Representing the Comprehension of Novelty in Open World Systems6. Deciding Machines: Moral-Scene Assessment for Intelligent Systems7. The Criticality of Social & Behavioral Science in the Development and Execution of Autonomous Systems8. Virtual Health and Artificial Intelligence: Using Technology to Improve Healthcare Delivery9. An Information Geometric look at the Valuing of Information10. AI, Autonomous Machines and Human Awareness: Towards Shared Machine-Human Contexts in Medicine11. Problems of Autonomous Agents following Informal, Open-textured Rules12. Engineering for Emergence in Information Fusion Systems: A Review of Some Challenges13. Integrating Expert Human Decision-Making in Artificial Intelligence Applications14. A Communication Paradigm for Human-Robot Interaction During Robot Failure Scenarios15. On Neural-Network Training Algorithms16. Identifying Distributed Incompetence in an Organization17. Begin with the human: Designing for safety and trustworthiness in cyber-physical systems18. Digital Humanities and Digital Economy19. Human-Machine Sense Making in Context Based Computational Decisions20. Constructing Mutual Context in Human-robot Collaborative Problem-solving with Multi-modal Input