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

This book focuses on the importance of human factors in the development of safe and reliable unmanned systems. It discusses current challenges such as how to improve the perceptual and cognitive abilities of robots, develop suitable synthetic vision systems, cope with degraded reliability in unmanned systems, predict robotic behavior in case of a loss of communication, the vision for future soldier–robot teams, human–agent teaming, real-world implications for human–robot interaction, and approaches to standardize both the display and control of technologies across unmanned systems. Based on the AHFE 2019 International Conference on Human Factors in Robots and Unmanned Systems, held on July 24–28, 2019, Washington D.C., USA, this book fosters new discussions and stimulates new advances in the development of more reliable, safer, and highly functional devices for carrying out automated and concurrent tasks.

The five-volume set CCIS 1832-1836 contains the extended abstracts of the posters presented during the 25th International Conference on Human-Computer Interaction, HCII 2023, which was held as a hybrid event in Copenhagen, Denmark, in July 2023. The total of 1578 papers and 396 posters included in the 47 HCII 2023 proceedings volumes were carefully reviewed and selected from the 7472 contributions.The posters presented in these five volumes are organized in topical sections as follows: Part I: ​HCI Design: Theoretical Approaches, Methods and Case Studies; Multimodality and Novel Interaction Techniques and Devices; Perception and Cognition in Interaction; Ethics, Transparency and Trust in HCI; User Experience and Technology Acceptance Studies.Part II: Supporting Health, Psychological Wellbeing, and Fitness; Design for All, Accessibility and Rehabilitation Technologies; Interactive Technologies for the Aging Population.Part III: ​Interacting with Data, Information and Knowledge; Learning and Training Technologies; Interacting with Cultural Heritage and Art.Part IV: ​Social Media: Design, User Experiences and Content Analysis; Advances in eGovernment Services; eCommerce, Mobile Commerce and Digital Marketing: Design and Customer Behavior; Designing and Developing Intelligent Green Environments; (Smart) Product Design.Part V: ​Driving Support and Experiences in Automated Vehicles; eXtended Reality: Design, Interaction Techniques, User Experience and Novel Applications; Applications of AI Technologies in HCI.

This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.

This book is based on the International Conference on Decision Economics (DECON 2019). Highlighting the fact that important decision-making takes place in a range of critical subject areas and research fields, including economics, finance, information systems, psychology, small and international business, management, operations, and production, the book focuses on analytics as an emerging synthesis of sophisticated methodology and large data systems used to guide economic decision-making in an increasingly complex business environment. DECON 2019 was organised by the University of Chieti-Pescara (Italy), the National Chengchi University of Taipei (Taiwan), and the University of Salamanca (Spain), and was held at the Escuela politécnica Superior de Ávila, Spain, from 26th to 28th June, 2019. Sponsored by IEEE Systems Man and Cybernetics Society, Spain Section Chapter, and IEEE Spain Section (Technical Co-Sponsor), IBM, Indra, Viewnext, Global Exchange, AEPIA-and-APPIA, with the funding supporting of the Junta de Castilla y León, Spain (ID: SA267P18-Project co-financed with FEDER funds)

The abundance of information and increase in computing power currently enable researchers to tackle highly complicated and challenging computational problems. Solutions to such problems are now feasible using advances and innovations from the area of Artificial Intelligence. The general focus of the AIAI conference is to provide insights on how Artificial Intelligence may be applied in real-world situations and serve the study, analysis and modeling of theoretical and practical issues. This volume contains papers selected for presentation at the 6th IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI 2010) and held in Larnaca, Cyprus, during October 6–7, 2010. IFIP AIAI 2010 was co-organized by the University of Cyprus and the Cyprus University of Technology and was sponsored by the Cyprus University of Technology, Frederick University and the Cyprus Tourism Organization. AIAI 2010 is the official conference of the WG12.5 “Artificial Intel- gence Applications” working group of IFIP TC12, the International Federation for Information Processing Technical Committee on Artificial Intelligence (AI). AIAI is a conference that grows in significance every year attracting researchers from different countries around the globe. It maintains high quality, standards and welcomes research papers describing technical advances and engineering and ind- trial applications of intelligent systems. AIAI 2010 was not confined to introducing how AI may be applied in real-life situations, but also included innovative methods, techniques, tools and ideas of AI expressed at the algorithmic or systemic level.

Innovation in Music: Technology and Creativity is a groundbreaking collection bringing together contributions from instructors, researchers, and professionals. Split into two sections, covering composition and performance, and technology and innovation, this volume offers truly international perspectives on ever-evolving practices. Including chapters on audience interaction, dynamic music methods, AI, and live electronic performances, this is recommended reading for professionals, students, and researchers looking for global insights into the fields of music production, music business, and music technology.

This book presents frontier research on the use of computational methods to model complex interactions in economics and finance. Artificial Intelligence, Machine Learning and simulations offer effective means of analyzing and learning from large as well as new types of data. These computational tools have permeated various subfields of economics, finance, and also across different schools of economic thought. Through 16 chapters written by pioneers in economics, finance, computer science, psychology, complexity and statistics/econometrics, the book introduces their original research and presents the findings they have yielded. Theoretical and empirical studies featured in this book draw on a variety of approaches such as agent-based modeling, numerical simulations, computable economics, as well as employing tools from artificial intelligence and machine learning algorithms. The use of computational approaches to perform counterfactual thought experiments are also introduced, which help transcend the limits posed by traditional mathematical and statistical tools. The book also includes discussions on methodology, epistemology, history and issues concerning prediction, validation, and inference, all of which have become pertinent with the increasing use of computational approaches in economic analysis.

Doctors, patients, investigators, administrators, and policymakers who assign diagnoses assume three elements: the name describes an entity with conceptual or evidentiary boundaries, the person setting the name has a high degree of certainty, and the name has a consensus definition. This book challenges this practice and offers an alternative to assigning diagnoses: quantitating diagnostic uncertainty in personal and public medical plans. This book offers the stakeholders' views participating in a workshop, sponsored by the Barbara Volcker Center/Hospital for Special Surgery, taking place in April 2020, about uncertain diagnoses. Chapters examine the circumstances in which diagnosis names are "unassignable", either because patients do not fit within diagnostic "boxes" or because health abnormalities evolve and change over time. In addition, the book deconstructs the processes of diagnosis and explores how different stakeholders used diagnosis names for various purposes. In examining pertinent questions, the book offers a roadmap to achieving consensus definitions or including measures of uncertainty in personal care, research, and policy. Diagnoses Without Names: Challenges for Medical Care, Research, and Policy is an essential resource for physicians and related professionals, residents, fellows, and graduate students in internal medicine, rheumatology, and clinical immunology as well as investigators, administrators, policymakers.