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Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C “actually caused” event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. The philosophy literature has been struggling with the problem of defining causality since Hume. In this book, Joseph Halpern explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression. Halpern applies and expands an approach to causality that he and Judea Pearl developed, based on structural equations. He carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation. He concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification.

This book examines new forms of nascent life that emerge through technical interactions within human-constructed environments—“machinic life”—in the sciences of cybernetics, artificial life, and artificial intelligence. With the development of such research initiatives as the evolution of digital organisms, computer immune systems, artificial protocells, evolutionary robotics, and swarm systems, it argues, machinic life has achieved a complexity and autonomy worthy of study in its own right. Drawing on the publications of scientists as well as a range of work in contemporary philosophy and cultural theory, but always with the primary focus on the “objects at hand”—the machines, programs, and processes that constitute machinic life—the book shows how they come about, how they operate, and how they are already changing. This understanding is a necessary first step, it further argues, that must precede speculation about the meaning and cultural implications of these new forms of life. Developing the concept of the “computational assemblage” (a machine and its associated discourse) as a framework to identify both resemblances and differences in form and function, the book offers a conceptual history of each of the three sciences. It considers the new theory of machines proposed by cybernetics from several perspectives, including Lacanian psychoanalysis and “machinic philosophy.” The book examines the history of the new science of artificial life and its relation to theories of evolution, emergence, and complex adaptive systems (as illustrated by a series of experiments carried out on various software platforms).

New technology has brought with it new tools for learning, and research has shown that the educational potential of video games resonates with scholars, teachers, and students alike. This book describes the largely untapped potential of mobile learning games—games played on such handheld devices as cell phones, Game Boys, and Sony PSPs—to make a substantial impact on learning. Examining mobile games from both educational and gaming perspectives, it argues that the strengths of the mobile platform—its portability, context sensitivity, connectivity, and ubiquity—make it ideal for learning games in elementary, secondary, university, and lifelong education. The book begins by exploring the past and present of education, educational technology, “edutainment,” and mobile games. It then offers a series of case studies of mobile educational games that have been developed and implemented in recent years. These games—either participatory (which require interaction with other players) or augmented reality (which augment the real world with virtual information)—can be produced at lower cost than PC or full-size console games. They use social dynamics and real-world context to enhance game play, can be integrated into the natural flow of instruction more easily than their big-screen counterparts, and can create compelling educational and engaging environments for learners. They are especially well suited for helping learners at every level develop twenty-first century skills, including the ability to tackle complex problems and acquire information in “just-in-time” fashion.

Most of the information available on cloud computing is either highly technical, with details that are irrelevant to non-technologists, or pure marketing hype, in which the cloud is simply used as a selling point. This book, however, explains the cloud from the user's viewpoint. The author explains what the cloud is, when to use it (and when not to), how to select a cloud service, how to integrate it with other technologies, and what the best practices are for using cloud computing. A simple and basic definition of cloud computing from the National Institute of Science and Technology is considered: a model enabling ubiquitous, convenient, on-demand access to a shared pool of configurable computing resources. Thus businesses, individuals and communities can harness information technology resources usually available only to large enterprises. This, as the author demonstrates, represents a paradigm shift for businesses and individuals alike. In additon, the book considers the contractual, legal, financial, security and risk related aspects of adopting and migrating to the cloud. Cloud patterns are examined in terms of five deployment models; and a cloud computing maturity model is derived to align the use of cloud computing with best practices.A unique aspect of the book is that it provides innovative constructs that affect the way cloud computing shall be viewed and used in the future. In particular, it addresses novel concepts for cloud computing: cloud cells, or specialist clouds for specific uses; the personal cloud; the cloud of things and services; and cloud service exchanges.

Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This book offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem; place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning; provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives); and present algorithms for covariate shift.

This book addresses the connections between computers, life, evolution, brains, and minds. Digital computers are recent and have changed our society. However, they represent just the latest way to process information, using algorithms to create order out of chaos. Before computers, the job of processing information was done by living organisms, which are nothing more than complex information processing devices, shaped by billions of years of evolution. The most advanced of these information processing devices is the human brain. Brains enable humans to process information in a way unparalleled by any other species, living or extinct, or by any existing machine. They provide humans with intelligence, consciousness and, some believe, even with a soul. Brains also enabled humans to develop science and technology to a point where it is possible to design computers with a power comparable to that of the human brain. Machine learning and artificial intelligence technologies will one day make it possible to create intelligent machines and computational biology will one day enable us to model, simulate, and understand biological systems and even complete brains, with unprecedented levels of detail. From these efforts, new minds will eventually emerge, minds that will emanate from the execution of programs running in powerful computers. These digital minds may one day rival our own, become our partners, and replace humans in many tasks. They may usher in a technological singularity, may make humans obsolete or even a threatened species. They make us super-humans or demi-gods.

Ubiquitous computing (or ubicomp) is the label for a “third wave” of computing technologies. Following the eras of the mainframe computer and the desktop PC, it is characterized by small and powerful computing devices that are worn, carried, or embedded in the world around us. The ubicomp research agenda originated at Xerox PARC in the late 1980s; these days, some form of that vision is a reality for the millions of users of Internet-enabled phones, GPS devices, wireless networks, and “smart” domestic appliances. This book explores the vision that has driven the ubiquitous computing research program and the contemporary practices which have emerged—both the motivating mythology and the everyday messiness of lived experience. Reflecting the interdisciplinary nature of the authors’ collaboration, it takes seriously the need to understand ubicomp not only technically but also culturally, socially, politically, and economically. The authors map the terrain of contemporary ubiquitous computing, in the research community and in daily life; explore dominant narratives in ubicomp around such topics as infrastructure, mobility, privacy, and domesticity; and suggest directions for future investigation, particularly with respect to methodology and conceptual foundations.

Logic-based formalizations of argumentation, which assume a set of formulae and then lay out arguments and counterarguments that can be obtained from these formulae, have been refined in recent years in an attempt to capture more closely real-world practical argumentation. This book introduces techniques for formalizing deductive argumentation in artificial intelligence, emphasizing emerging formalizations for practical argumentation. It discusses how arguments can be constructed, how key intrinsic and extrinsic factors can be identified, and how these analyses can be harnessed for formalizing argumentation for use in real-world problem analysis and decision making. The book focuses on a monological approach to argumentation, in which there is a set of possibly conflicting pieces of information (each represented by a formula) that has been collated by an agent or a pool of agents. The role of argumentation is to construct a collection of arguments and counterarguments pertaining to some particular claim of interest to be used for analysis or presentation. The book elucidates and formalizes key elements of deductive argumentation.

Despite the emergence of computer games as a dominant cultural industry (and the accompanying emergence of computer games as the subject of scholarly research), we know little or nothing about the ethics of computer games. Considerations of the morality of computer games seldom go beyond intermittent portrayals of them in the mass media as training devices for teenage serial killers. This exploration of the subject addresses broader issues about the ethics of games, the ethics of playing the games, and the ethical responsibilities of game designers. It argues that computer games are ethical objects, that computer game players are ethical agents, and that the ethics of computer games should be seen as a complex network of responsibilities and moral duties. Players should not be considered passive amoral creatures; they reflect, relate, and create with ethical minds. The games they play are ethical systems, with rules that create gameworlds with values at play. Drawing on concepts from philosophy and game studies, the book proposes a framework for analyzing the ethics of computer games as both designed objects and player experiences. After presentin core theoretical arguments and offering a general theory for understanding computer game ethics, the book offers case studies examining single-player games (using Bioshock as an example), multiplayer games (illustrated by Defcon), and online gameworlds (illustrated by World of Warcraft) from an ethical perspective. It explores issues raised by unethical content in computer games and its possible effect on players.

Digital games tend to follow one of two trends when presenting game information to the player. The game may present game information in a naturalistic way as part of the imaginary universe presented by the game, avoiding symbolic or abstract representations that seem alien to the fictional world. Alternatively, the game may use graphical augmentations such as superimposed information, menus, and icons that points directly to important information. In Gameworld Interfaces, Kristine Jørgensen investigates different approaches to designing the game interface. She argues that gameworlds are not governed by the pursuit of fictional coherence but by the logics of game mechanics, and that this is a characteristic that distinguishes gameworlds from traditional fictional worlds. For this reason, one approach to interface design is not superior to the other as long as relevant game information is effectively communicated in a way that makes sense to the particular game situation. Gameworld Interfaces is a study of audiovisual computer game interfaces and how they interact with the gameworld. The book presents a theory of game user interfaces and considers the implications of this theory for game design based on interviews with game interface design teams, extensive interviews and observations of players, and many hours of gameplay.

In South Korea, online gaming is a cultural phenomenon. Games are broadcast on television, professional gamers are celebrities, and youth culture is often identified with online gaming. Uniquely in the online games market, Korea not only dominates the local market but has also made its mark globally. This book examines the rapid growth of this industry from a political economy perspective, discussing it in social, cultural, and economic terms. Korea has the largest percentage of broadband subscribers of any country in the world, and Koreans spend increasing amounts of time and money on Internet-based games. Online gaming has become a mode of socializing—a channel for human relationships. The Korean online game industry has been a pioneer in software development and eSports (electronic sports and leagues). The book discusses the policies of the Korean government that encouraged the development of online gaming both as a cutting-edge business and as a cultural touchstone; the impact of economic globalization; the relationship between online games and Korean society; and the future of the industry. It examines the rise of Korean online games in the global marketplace, the emergence of eSport as a youth culture phenomenon, the working conditions of professional gamers, the role of game fans as consumers, how Korea’s local online game industry has become global, and whether these emerging firms have challenged the West’s dominance in global markets.

The Internet gives us access to a wealth of information in languages we don’t understand. The investigation of automated or semi-automated approaches to translation has become a thriving research field with enormous commercial potential. This book investigates how Machine Learning techniques can improve Statistical Machine Translation, currently at the forefront of research in the field. It looks first at enabling technologies—technologies that solve problems which are not Machine Translation proper but are linked closely to the development of a Machine Translation system. These include the acquisition of bilingual sentence-aligned data from comparable corpora, automatic construction of multilingual name dictionaries, and word alignment. The book then presents new or improved statistical Machine Translation techniques, including a discriminative training framework for leveraging syntactic information, the use of semi-supervised and kernel-based learning methods, and the combination of multiple Machine Translation outputs in order to improve overall translation quality.

As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning’s greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the book discusses topics that include learning under covariate shift, model selection, importance estimation, and active learning. It describes such real-world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images.

Over the last decade, machinima—the use of computer game engines to create movies—has emerged as a vibrant area in digital culture. Machinima as a filmmaking tool grew from the bottom up, driven by enthusiasts who taught themselves to deploy technologies from computer games to create animated films quickly and cheaply. This book provides a critical overview of this rapidly developing field. The contributors include both academics and artist-practitioners. They explore machinima from multiple perspectives, ranging from technical aspects of machinima, from real-time production to machinima as a performative and cinematic medium, while paying close attention to the legal, cultural, and pedagogical contexts for machinima. The book extends critical debates originating within the machinima community to a wider audience and provides a foundation for scholarly work from a variety of disciplines. It charts the emergence of machinima as a game-based cultural production that spans technologies and media, forming new communities of practice on its way to a history, an aesthetic, and a market.

The idea of intelligent machines has become part of popular culture. But tracing the history of the actual science of machine intelligence reveals a rich network of cross-disciplinary contributions—the unrecognized origins of ideas now central to artificial intelligence, artificial life, cognitive science, and neuroscience. In this book, scientists, artists, historians, and philosophers discuss the multidisciplinary quest to formalize and understand the generation of intelligent behavior in natural and artificial systems as a wholly mechanical process. The chapters illustrate the diverse and interacting notions that chart the evolution of the idea of the mechanical mind. They describe the mechanized mind as, among other things, an analogue system, an organized suite of chemical interactions, a self-organizing electromechanical device, an automated general-purpose information processor, and an integrated collection of symbol-manipulating mechanisms. The chapters investigate the views of pivotal figures that range from Descartes and Heidegger to Alan Turing and Charles Babbage, and emphasize such frequently overlooked areas as British cybernetic and pre-cybernetic thinkers. The book concludes with the personal insights of five highly influential figures in the field: John Maynard Smith, John Holland, Oliver Selfridge, Horace Barlow, and Jack Cowan.

The capacity to think about our own thinking may lie at the heart of what it means to be both human and intelligent. Philosophers and cognitive scientists have investigated these matters for many years. Researchers in artificial intelligence have gone further, attempting to implement actual machines that mimic, simulate, and perhaps even replicate this capacity, called metareasoning. This book offers a variety of perspectives—drawn from philosophy, cognitive psychology, and computer science—on reasoning about the reasoning process. It offers a simple model of reasoning about reason as a framework for its discussions. Following this framework, the contributors consider metalevel control of computational activities, introspective monitoring, distributed metareasoning, and, putting all these aspects of metareasoning together, models of the self. Taken together, the chapters offer an integrated narrative on metareasoning themes from both artificial intelligence and cognitive science perspectives.

This book tells two closely-related stories: first, how the digital cloud grew out of much older networks, such as television, the railroad, and the sewer system; and second, how the cloud grafts digital technologies onto older ways of exerting power over a population (such as state violence and torture). With the latest revelations about National Security Agency surveillance, readers are increasingly aware that the cloud represents politically contested terrain. While typical responses to this debate invoke technological and legal solutions, such as do-not-track software or a new law, this book takes an alternate approach. The perspective of media studies, and, more generally, understanding the cloud as a cultural fantasy, situates these vital debates within a wider American political and social context. It allows readers to understand why discussions of threats to the ‘free’ Internet, such as spam and hackers, often invoke the specter of foreignness (e.g. China, Iran, Nigeria); why Cold War rhetoric has increasingly informed digital threats, as in the New York Times’s invention of the phrase “mutually assured cyberdestruction”; and even why the NSA’s facilities for decrypting intercepted messages are often identical to those used by archivists trying to place digital media into cold storage. By locating the materiality of the cloud within the discourses of security and participation in postwar America, A Prehistory of the Cloud offers a set of new tools for rethinking today’s digital environment.

Cloud computing has emerged as one of the most salient recent developments in information technology. Predictions about its future run the gamut, with some believing that it represents a fundamental change the nature of computing and others arguing that it is nothing more than overhyped repackaging of existing technologies. This book represents the first effort to bridge this gap by exploring cloud computing’s implications from a wide range of interdisciplinary perspectives. Individual chapters analyze the business case for cloud computing, the changing nature of reliability in the Internet, the implications of treating the Internet as critical infrastructure, architectural changes to make cloud computing more contractible, and the impact of cloud computing on copyright, privacy, and consumer protection.

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. It first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms which perform two-step learning. It then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of benchmark experiments. Finally, the book looks at interesting directions for SSL research. It closes with a discussion of the relationship between semi-supervised learning and transduction.

This book guides students through an exploration of the idea that thinking might be understood as a form of computation. Students make the connection between thinking and computing by learning to write computer programs for a variety of tasks that require thought, including solving puzzles, understanding natural language, recognizing objects in visual scenes, planning courses of action, and playing strategic games. The material is presented with minimal technicalities and is accessible to undergraduate students with no specialized knowledge or technical background beyond high school mathematics. Students use Prolog, learning to express what they need as a Prolog program and letting Prolog search for answers. After an introduction to the basic concepts, the book offers three chapters on Prolog, covering back-chaining, programs and queries, and how to write the sorts of Prolog programs used in the book. The book follows this with case studies of tasks that appear to require thought, then looks beyond Prolog to consider learning, explaining, and propositional reasoning. Most of the chapters conclude with short bibliographic notes and exercises.