Robust Intelligence And Trust In Autonomous Systems

This volume explores the intersection of robust intelligence (RI) and trust in autonomous systems across multiple contexts among autonomous hybrid systems, where hybrids are arbitrary combinations of humans, machines and robots. To better understand the relationships between artificial intelligence (AI) and RI in a way that promotes trust between autonomous systems and human users, this book explores the underlying theory, mathematics, computational models, and field applications. It uniquely unifies the fields of RI and trust and frames it in a broader context, namely the effective integration of human-autonomous systems. A description of the current state of the art in RI and trust introduces the research work in this area. With this foundation, the chapters further elaborate on key research areas and gaps that are at the heart of effective human-systems integration, including workload management, human computer interfaces, team integration and performance, advanced analytics, behavior modeling, training, and, lastly, test and evaluation. Written by international leading researchers from across the field of autonomous systems research, Robust Intelligence and Trust in Autonomous Systems dedicates itself to thoroughly examining the challenges and trends of systems that exhibit RI, the fundamental implications of RI in developing trusted relationships with present and future autonomous systems, and the effective human systems integration that must result for trust to be sustained. Contributing authors: David W. Aha, Jenny Burke, Joseph Coyne, M.L. Cummings, Munjal Desai, Michael Drinkwater, Jill L. Drury, Michael W. Floyd, Fei Gao, Vladimir Gontar, Ayanna M. Howard, Mo Jamshidi, W.F. Lawless, Kapil Madathil, Ranjeev Mittu, Arezou Moussavi, Gari Palmer, Paul Robinette, Behzad Sadrfaridpour, Hamed Saeidi, Kristin E. Schaefer, Anne Selwyn, Ciara Sibley, Donald A. Sofge, Erin Solovey, Aaron Steinfeld, Barney Tannahill, Gavin Taylor, Alan R. Wagner, Yue Wang, Holly A. Yanco, Dan Zwillinger.

This book establishes the foundations needed to realize the ultimate goals for artificial intelligence, such as autonomy and trustworthiness. Aimed at scientists, researchers, technologists, practitioners, and students, it brings together contributions offering the basics, the challenges and the state-of-the-art on trusted autonomous systems in a single volume. The book is structured in three parts, with chapters written by eminent researchers and outstanding practitioners and users in the field. The first part covers foundational artificial intelligence technologies, while the second part covers philosophical, practical and technological perspectives on trust. Lastly, the third part presents advanced topics necessary to create future trusted autonomous systems. The book augments theory with real-world applications including cyber security, defence and space.

Providing a high level of autonomy for a human-machine team requires assumptions that address behavior and mutual trust. The performance of a human-machine team is maximized when the partnership provides mutual benefits that satisfy design rationales, balance of control, and the nature of autonomy. The distinctively different characteristics and features of humans and machines are likely why they have the potential to work well together, overcoming each other's weaknesses through cooperation, synergy, and interdependence which forms a “collective intelligence. Trust is bidirectional and two-sided; humans need to trust AI technology, but future AI technology may also need to trust humans.Putting AI in the Critical Loop: Assured Trust and Autonomy in Human-Machine Teams focuses on human-machine trust and “assured performance and operation in order to realize the potential of autonomy. This book aims to take on the primary challenges of bidirectional trust and performance of autonomous systems, providing readers with a review of the latest literature, the science of autonomy, and a clear path towards the autonomy of human-machine teams and systems. Throughout this book, the intersecting themes of collective intelligence, bidirectional trust, and continual assurance form the challenging and extraordinarily interesting themes which will help lay the groundwork for the audience to not only bridge the knowledge gaps, but also to advance this science to develop better solutions. Assesses the latest research advances, engineering challenges, and the theoretical gaps surrounding the question of autonomy Reviews the challenges of autonomy (e.g., trust, ethics, legalities, etc.), including gaps in the knowledge of the science Offers a path forward to solutions Investigates the value of trust by humans of HMTs, as well as the bidirectionality of trust, understanding how machines learn to trust their human teammates

This book constitutes the thoroughly refereed post-conference proceedings of the 8th International Conference on Modelling and Simulation for Autonomous Systems, MESAS 2021, held as a virtual event due COVID-19, in October 2021. The 30 full papers together with 2 short papers included in the volume were carefully reviewed and selected from 50 submissions. They are organized in the following topical sections: M&S of intelligent systems, R&D and application; and AxS/AI in context of future warfare and security environment and future challenges of Advance M&S Technology.

This book is devoted to the examination of emerging practical issues related to automated and autonomous systems. The book highlights the significance of these emergent technologies that determine the course of our daily lives. Each unique chapter highlights human factors and engineering concerns across real-world applications, including matters related to aviation and healthcare, human-robot interaction, transportation systems, cybersecurity and cyber defense. This book also depicts the boundaries that separate humans from machine as we continue to become ever more immersed in and symbiotic with these fast-emerging technologies. Automation, across many occupations, has transitioned the human to a role of monitoring machines, presenting challenges related to vigilance and workload. This book identifies the importance of an approach to automated technology that emphasizes the "human user" at the center of the design process. Features Provides perspectives on the role of the individual and teams in complex technical systems such as aviation, healthcare, and medicine Presents the development of highly autonomous systems related to human safety and performance Examines solutions to human factors challenges presented by modern threats to data privacy and cybersecurity Discusses human perceptual and cognitive capabilities underwriting to the design of automated and autonomous systems • Provides in-depth, expert reviews of context-related developments in automation and human-robot teaming Human Performance in Automated and Autonomous Systems: Emerging Issues and Practical Perspectives applies scientific theory directly to real-world systems where automation and autonomous technology is implemented.

This two-volume set addresses a variety of human factors issues and engineering concerns across various real-world applications such as aviation and driving, cybersecurity, and healthcare systems. The contents of these books also present recent theories and methods related to human performance, workload and usability assessment in automated and autonomous systems. In this set, the authors discuss both current and developing topics of advanced automation technologies and present emerging practical challenges. Topics covered include unmanned aerial systems and self-driving cars, individual and team performance, human-robot interaction, and operator selection and training. Both practical and theoretical discussions of modern automated and autonomous systems are provided throughout each of the volumes. These books are suitable for those first approaching the issues to those well versed in these fast-moving areas, including students, teachers, researchers, engineers, and policy makers alike. Volume 1 - Human Performance in Automated and Autonomous Systems: Current Theory and Methods Volume 2 - Human Performance in Automated and Autonomous Systems: Emerging Issues and Practical Perspectives

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

The importance of databases and information systems to the functioning of 21st century life is indisputable. This book presents papers from the 13th International Baltic Conference on Databases and Information Systems, held in Trakai, Lithuania, from 1- 4 July 2018. Since the first of these events in 1994, the Baltic DB&IS has proved itself to be an excellent forum for researchers, practitioners and PhD students to deliver and share their research in the field of advanced information systems, databases and related areas. For the 2018 conference, 69 submissions were received from 15 countries. Each paper was assigned for review to at least three referees from different countries. Following review, 24 regular papers were accepted for presentation at the conference, and from these presented papers the 14 best-revised papers have been selected for publication in this volume, together with a preface and three invited papers written by leading experts. The selected revised and extended papers present original research results in a number of subject areas: information systems, requirements and ontology engineering; advanced database systems; internet of things; big data analysis; cognitive computing; and applications and case studies. These results will contribute to the further development of this fast-growing field, and will be of interest to all those working with advanced information systems, databases and related areas.