This book is a state of the art resource that robotics researchers and engineers can use to make their robots and autonomous vehicles smarter. Readers will be able to describe metareasoning, select an appropriate metareasoning approach, and synthesize metareasoning policies. Metareasoning for Robots adopts a systems engineering perspective in which metareasoning is an approach that can improve the overall robot or autonomous system, not just one component or subsystem. This book introduces key concepts, discusses design options for metareasoning approaches and policies, and presents approaches for testing and evaluation of metareasoning policies. After considering the conceptual design phase, it discusses how to implement metareasoning in the robot’s software architecture and how to synthesize metareasoning policies. Every chapter has references to valuable works on robotics and metareasoning, and the book uses examples from the author’s own research and from other research groups to illustrate these ideas. In addition, this book provides links to books and papers for readers who wish to investigate these topics further.

Experts report on the latest artificial intelligence research concerning reasoning about reasoning itself.

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.

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

Robots may one day rule the world, but what is a robot-ruled Earth like? Many think that the first truly smart robots will be brain emulations or "ems." Robin Hanson draws on decades of expertise in economics, physics, and computer science to paint a detailed picture of this next great era in human (and machine) evolution - the age of em.

Intelligent autonomous systems are emerged as a key enabler for the creation of a new paradigm of services to humankind, as seen by the recent advancement of autonomous cars licensed for driving in our streets, of unmanned aerial and underwater vehicles carrying out hazardous tasks on-site, and of space robots engaged in scientific as well as operational missions, to list only a few. This book aims at serving the researchers and practitioners in related fields with a timely dissemination of the recent progress on intelligent autonomous systems, based on a collection of papers presented at the 12th International Conference on Intelligent Autonomous Systems, held in Jeju, Korea, June 26-29, 2012. With the theme of “Intelligence and Autonomy for the Service to Humankind, the conference has covered such diverse areas as autonomous ground, aerial, and underwater vehicles, intelligent transportation systems, personal/domestic service robots, professional service robots for surgery/rehabilitation, rescue/security and space applications, and intelligent autonomous systems for manufacturing and healthcare. This volume 2 includes contributions devoted to Service Robotics and Human-Robot Interaction and Autonomous Multi-Agent Systems and Life Engineering.