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We are increasingly seeing computer systems which are expected to function without operator intervention. This is perhaps acceptable for running computer networks or traffic lights; however, we are now seeing computer systems deployed to qualitatively influence human judgments such as rulings on legal disputes or fitness for work to evaluate disability benefits. In keeping with the precautionary principle, it is important that those who are developing this capability — technologists and scientists — think through its potential implications.The aim of this book is to explore the technological and social and implications of computers and robots becoming increasingly ‘aware’ of their environment and the people in it, and their being increasingly ‘self-aware’ of their own existence within it.The wide-ranging scope of the text covers three different angles of the concept of ‘the computer after me’: (1) the next generation of computationally powerful aware systems; (2) systems in which the computer is aware of qualitatively impact human concerns such as law, health and rules; and (3) computers and robots which are aware of themselves.
This book provides formal and informal definitions and taxonomies for self-aware computing systems, and explains how self-aware computing relates to many existing subfields of computer science, especially software engineering. It describes architectures and algorithms for self-aware systems as well as the benefits and pitfalls of self-awareness, and reviews much of the latest relevant research across a wide array of disciplines, including open research challenges. The chapters of this book are organized into five parts: Introduction, System Architectures, Methods and Algorithms, Applications and Case Studies, and Outlook. Part I offers an introduction that defines self-aware computing systems from multiple perspectives, and establishes a formal definition, a taxonomy and a set of reference scenarios that help to unify the remaining chapters. Next, Part II explores architectures for self-aware computing systems, such as generic concepts and notations that allow a wide range of self-aware system architectures to be described and compared with both isolated and interacting systems. It also reviews the current state of reference architectures, architectural frameworks, and languages for self-aware systems. Part III focuses on methods and algorithms for self-aware computing systems by addressing issues pertaining to system design, like modeling, synthesis and verification. It also examines topics such as adaptation, benchmarks and metrics. Part IV then presents applications and case studies in various domains including cloud computing, data centers, cyber-physical systems, and the degree to which self-aware computing approaches have been adopted within those domains. Lastly, Part V surveys open challenges and future research directions for self-aware computing systems. It can be used as a handbook for professionals and researchers working in areas related to self-aware computing, and can also serve as an advanced textbook for lecturers and postgraduate students studying subjects like advanced software engineering, autonomic computing, self-adaptive systems, and data-center resource management. Each chapter is largely self-contained, and offers plenty of references for anyone wishing to pursue the topic more deeply.
This book describes state-of-the-art approaches to Fog Computing, including the background of innovations achieved in recent years. Coverage includes various aspects of fog computing architectures for Internet of Things, driving reasons, variations and case studies. The authors discuss in detail key topics, such as meeting low latency and real-time requirements of applications, interoperability, federation and heterogeneous computing, energy efficiency and mobility, fog and cloud interplay, geo-distribution and location awareness, and case studies in healthcare and smart space applications.
A collective autonomic system consists of collaborating autonomic entities which are able to adapt at runtime, adjusting to the state of the environment and incorporating new knowledge into their behavior. These highly dynamic systems are also known as ensembles. To ensure correct behavior of ensembles it is necessary to support their development through appropriate methods and tools which can guarantee that an autonomic system lives up to its intended purpose; this includes respecting important constraints of the environment. This State-of-the-Art Survey addresses the engineering of such systems by presenting the methods, tools and theories developed within the ASCENS project. ASCENS was an integrated project funded in the period 2010-2015 by the 7th Framework Programme (FP7) of the European Commission as part of the Future Emerging Technologies Proactive Initiative (FET Proactive). The 17 contributions included in this book are organized in four parts corresponding to the research areas of the project and their concrete applications: (I) language and verification for self-awareness and self-expression, (II) modeling and theory of self-aware and adaptive systems, (III) engineering techniques for collective autonomic systems, and last but not least, (IV) challenges and feedback provided by the case studies of the project in the areas of swarm robotics, cloud computing and e-mobility.
Did you know that computation can be implemented with cytoskeleton networks, chemical reactions, liquid marbles, plants, polymers and dozens of other living and inanimate substrates? Do you know what is reversible computing or a DNA microscopy? Are you aware that randomness aids computation? Would you like to make logical circuits from enzymatic reactions? Have you ever tried to implement digital logic with Minecraft? Do you know that eroding sandstones can compute too?This volume reviews most of the key attempts in coming up with an alternative way of computation. In doing so, the authors show that we do not need computers to compute and we do not need computation to infer. It invites readers to rethink the computer and computing, and appeals to computer scientists, mathematicians, physicists and philosophers. The topics are presented in a lively and easily accessible manner and make for ideal supplementary reading across a broad range of subjects.
There is an emerging view, supported by animal welfare legislation in a number of countries, that some advanced invertebrates are self-aware, sentient beings with the ability to feel pain. Sentience must encompass elements of time and neural complexity, including memory and learning, which leads us to ask: At what convergent point in the evolution of nervous systems does the subjective sensation of pain arise? Here we start to grapple with this issue, particularly with regard to arthropods and cephalopod molluscs, and to consider the most appropriate ways of anesthetizing them to minimize pain wherever possible. We also report on the development of cell culture techniques to understand the actions of the anesthetics being used. A better understanding of sentient creatures, other than ourselves, may eventually assist future development of artificial intelligence, particularly if we are able to perceive whatever common neural features underlie sentience in those animals that possess it.
The LNCS Transactions on Foundations for Mastering Change, FoMaC, aims to establish a forum for formal-methods-based research, dealing with the nature of today’s agile system development, which is characterized by unclear premises, unforeseen change, and the need for fast reaction, in a context of hard-to-control frame conditions, such as third-party components, network problems, and attacks. Submissions are evaluated according to these goals. This book, the first volume in the series, contains contributions by the members of the editorial board. These contributions indicate the envisioned style and range of papers of topics covered by the transactions series. They cross-cut various traditional research directions and are characterized by a clear focus on change.
This book stages a dialogue between international researchers from the broad fields of complexity science and narrative studies. It presents an edited collection of chapters on aspects of how narrative theory from the humanities may be exploited to understand, explain, describe, and communicate aspects of complex systems, such as their emergent properties, feedbacks, and downwards causation; and how ideas from complexity science can inform narrative theory, and help explain, understand, and construct new, more complex models of narrative as a cognitive faculty and as a pervasive cultural form in new and old media. The book is suitable for academics, practitioners, and professionals, and postgraduates in complex systems, narrative theory, literary and film studies, new media and game studies, and science communication.
This book offers a unique interdisciplinary perspective on the ethics of 'artificial intelligence' – autonomous, intelligent, (and connected) systems, or AISs, applying principles of social cognition to understand the social and ethical issues associated with the creation, adoption, and implementation of AISs. As humans become entangled in sociotechnical systems defined by human and artificial agents, there is a pressing need to understand how trust is created, used, and abused. Compounding the difficulty in answering these questions, stakeholders directly or indirectly affected by these systems differ in their motivations, understanding, and values. This volume provides a comprehensive resource to help stakeholders understand ethical issues of designing and implementing AISs using an ethical sensemaking approach. Starting with the general technical affordances of AIS, Dr. Jordan Richard Schoenherr considers the features of system design relating data integrity, selection and interpretation of algorithms, and the evolution processes that drive AISs innovation as a sociotechnological system. The poles of technophobia (algorithmic aversion) and technophilia (algorithmic preference) in the public perception of AISs are then described and considered against existing evidence, including issues ranging from the displacement and re-education needs of the human workforce, the impact of use of technology on interpersonal accord, and surveillance and cybersecurity. Ethical frameworks that provide tools for evaluating the values and outcomes of AISs are then reviewed, and how they can be aligned with ethical sensemaking processes identified by psychological science is explored. Finally, these disparate threads are brought together in a design framework. Also including sections on policies and guideline, gaming and social media, and Eastern philosophical frameworks, this is fascinating reading for students and academics in psychology, computer science, philosophy, and related areas, as well as professionals such as policy makers and those working with AI systems.