This book constitutes the proceedings of the First International Symposium, ToM for Teams 2021, held in Washington, DC, USA, during November 4–6, 2021, Each chapter in this section tackles a different aspect of AI representing the thoughts and beliefs of human agents. The work presented herein represents our collective efforts to better understand ToM, develop AI with ToM capabilities (ASI), and study how to integrate such systems into human teams.
Proceedings of the AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2023 Hawaii Edition), Honolulu, Hawaii, USA 4-6, December 2023
This double volume book set constitutes the refereed proceedings of 4th International Conference, AI-HCI 2023, held as part of the 25th International Conference, HCI International 2023, which was held virtually in Copenhagen, Denmark in July 2023. The total of 1578 papers and 396 posters included in the HCII 2023 proceedings was carefully reviewed and selected from 7472 submissions. The first volume focuses on topics related to Human-Centered Artificial Intelligence, explainability, transparency and trustworthiness, ethics and fairness, as well as AI-supported user experience design. The second volume focuses on topics related to AI for language, text, and speech-related tasks, human-AI collaboration, AI for decision-support and perception analysis, and innovations in AI-enabled systems.
Human-Machine Shared Contexts considers the foundations, metrics, and applications of human-machine systems. Editors and authors debate whether machines, humans, and systems should speak only to each other, only to humans, or to both and how. The book establishes the meaning and operation of "shared contexts between humans and machines; it also explores how human-machine systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems composed of humans and machines. This book explores how user interventions may improve the context for autonomous machines operating in unfamiliar environments or when experiencing unanticipated events; how autonomous machines can be taught to explain contexts by reasoning, inferences, or causality, and decisions to humans relying on intuition; and for mutual context, how these machines may interdependently affect human awareness, teams and society, and how these "machines" may be affected in turn. In short, can context be mutually constructed and shared between machines and humans? The editors are interested in whether shared context follows when machines begin to think, or, like humans, develop subjective states that allow them to monitor and report on their interpretations of reality, forcing scientists to rethink the general model of human social behavior. If dependence on machine learning continues or grows, the public will also be interested in what happens to context shared by users, teams of humans and machines, or society when these machines malfunction. As scientists and engineers "think through this change in human terms," the ultimate goal is for AI to advance the performance of autonomous machines and teams of humans and machines for the betterment of society wherever these machines interact with humans or other machines. This book will be essential reading for professional, industrial, and military computer scientists and engineers; machine learning (ML) and artificial intelligence (AI) scientists and engineers, especially those engaged in research on autonomy, computational context, and human-machine shared contexts; advanced robotics scientists and engineers; scientists working with or interested in data issues for autonomous systems such as with the use of scarce data for training and operations with and without user interventions; social psychologists, scientists and physical research scientists pursuing models of shared context; modelers of the internet of things (IOT); systems of systems scientists and engineers and economists; scientists and engineers working with agent-based models (ABMs); policy specialists concerned with the impact of AI and ML on society and civilization; network scientists and engineers; applied mathematicians (e.g., holon theory, information theory); computational linguists; and blockchain scientists and engineers. - Discusses the foundations, metrics, and applications of human-machine systems - Considers advances and challenges in the performance of autonomous machines and teams of humans - Debates theoretical human-machine ecosystem models and what happens when machines malfunction
A fascinating exploration of the theories and arguments surrounding the notions of thought and representation. Now in its 2nd edition, Cranes's classic text has introduced thousands to some of the most important ideas in philosophy of mind.
Team collaboration involves many operational tasks such as team decision-making or course of action selection, developing shared understanding, and intelligence analysis. These operational tasks must be performed in many situations, often under severe time pressure, with information and knowledge uncertainty, large amounts of dynamic information and across different team characteristics. Recent research in this area has focused on various aspects of human collaborative decision-making and the underlying cognitive processes while describing those processes at different levels of detail, making it difficult to compare research results. The theoretical construct of ’macrocognition in teams’ was developed to facilitate cognitive research in team collaboration, which will enable a common level of understanding when defining, measuring and discussing the cognitive processes in team collaboration. Macrocognition is defined as both the internalized and externalized mental processes employed by team members in complex, one-of-a-kind, collaborative problem solving. Macrocognition in Teams provides readers with a greater understanding of the macrocognitive processes which support collaborative team activity, showcasing current research, theories, methodologies and tools. It will be of direct relevance to academics, researchers and practitioners interested in group/team interaction, performance, development and training.
This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXII International Conference on Neuroinformatics, held on October 12-16, 2020, Moscow, Russia.
