This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Knowledge, Information, and Creativity Support Systems, KCIS 2010, held in Chang Mai, Thailand, in November 2010. The 23 revised full papers presented were carefully reviewed and selected from 72 submissions. The papers cover a broad range of topics related to all knowledge science-related areas including creativity support, decision science, knowledge science, data mining, machine learning, databases, statistics, knowledge acquisition, automatic scientific discovery, data/knowledge visualization, and knowledge-based systems.
Clark charts a fundamental shift from a static, inner-code-oriented conception of the subject matter of cognitive science to a more dynamic, developmentally rich, process-oriented view.
Connectionism is a “hands on” introduction toconnectionist modeling through practical exercises in differenttypes of connectionist architectures. explores three different types of connectionist architectures– distributed associative memory, perceptron, and multilayerperceptron provides a brief overview of each architecture, a detailedintroduction on how to use a program to explore this network, and aseries of practical exercises that are designed to highlight theadvantages, and disadvantages, of each accompanied by a website athttp://www.bcp.psych.ualberta.ca/~mike/Book3/ that includespractice exercises and software, as well as the files and blankexercise sheets required for performing the exercises designed to be used as a stand-alone volume or alongsideMinds and Machines: Connectionism and Psychological Modeling(by Michael R.W. Dawson, Blackwell 2004)
Economics is changing. In the last few years it has generated a number of new approaches. One of the most promising - complexity economics - was pioneered in the 1980s and 1990s by a small team at the Santa Fe Institute. Economist and complexity theorist W. Brian Arthur led that team, and in this book he collects many of his articles on this new approach. The traditional framework sees behavior in the economy as in an equilibrium steady state. People in the economy face well-defined problems and use perfect deductive reasoning to base their actions on. The complexity framework, by contrast, sees the economy as always in process, always changing. People try to make sense of the situations they face using whatever reasoning they have at hand, and together create outcomes they must individually react to anew. The resulting economy is not a well-ordered machine, but a complex evolving system that is imperfect, perpetually constructing itself anew, and brimming with vitality. The new vision complements and widens the standard one, and it helps answer many questions: Why does the stock market show moods and a psychology? Why do high-tech markets tend to lock in to the dominance of one or two very large players? How do economies form, and how do they continually alter in structure over time? The papers collected here were among the first to use evolutionary computation, agent-based modeling, and cognitive psychology. They cover topics as disparate as how markets form out of beliefs; how technology evolves over the long span of time; why systems and bureaucracies get more complicated as they evolve; and how financial crises can be foreseen and prevented in the future.
Introduction: Adaptation, Evolution, and Intelligence, Lashon Booker, Stephanie Forrest, Melanie Mitchell, and Rick Riolo. PART 1: GENETIC ALGOROTHMS AND BEYOND. 1. Genetic Algorithms: A 30 Year Perspective, Kenneth DeJong. 2. Human-Competitive Machine Intelligence by Means of Genetic Algorithms, John R. Koza. 3. John Holland, Facetwise models, and Economy of Thought, David E. Goldberg. PART 2: COMPUTATION, ARTIFICIAL INTELLIGENCE, AND BEYOND. 4. An Early Graduate Program in Computers and Communications, Arthur W. Burks. 5. Had We But World Enough and Time, Oliver G. Selfridge. 6. Discrete Eve.
Cognitive science has been dominated by a model of mental phenomena based on software--or the rules for input, output, organization, and functioning employed by a computer--which is now showing signs of losing its preeminence. In this book 28 leading scholars from diverse fields carefully consider what that think will be the future course for this intellectual movement.
Artificial Intelligence is the study of how to build or program computers to enable them to do what minds can do. This volume discusses the ways in which computational ideas and computer modeling can aid our understanding of human and animal minds. Major theoretical approaches are outlined, as well as some promising recent developments. Fundamental philosophical questions are discussed along with topics such as: the differences between symbolic and connectionist AI, planning and problem solving, knowledge representation, learning, expert systems, vision, natural language, creativity, and human-computer interaction. This volume is suitable for any psychologist, philosopher, or computer scientist wanting to know the current state of the art in this area of cognitive science. - Up-to-date account of how computational ideas and techniques are relevant to psychology - Includes discussions of "classical" (symbolic) AI, of connectionism (neural nets), of evolutionary programming, and of A-Life - Discusses a wide range of psychology from low-level vision to creativity
The basic idea of the particular way of understanding mental phenomena that has inspired the "cognitive revolution" is that, as a result of certain relatively recent intellectual and technological innovations, informed theorists now possess a more powerfully insightful comparison or model for mind than was available to any thinkers in the past. The model in question is that of software, or the list of rules for input, output, and internal transformations by which we determine and control the workings of a computing machine's hardware. Although this comparison and its many implications have dominated work in the philosophy, psychology, and neurobiology of mind since the end of the Second World War, it now shows increasing signs of losing its once virtually unquestioned preeminence. Thus we now face the question of whether it is possible to repair and save this model by means of relatively inessential "tinkering", or whether we must reconceive it fundamentally and replace it with something different. In this book, twenty-eight leading scholars from diverse fields of "cognitive science"-linguistics, psychology, neurophysiology, and philosophy- present their latest, carefully considered judgements about what they think will be the future course of this intellectual movement, that in many respects has been a watershed in our contemporary struggles to comprehend that which is crucially significant about human beings. Jerome Bruner, Noam Chomsky, Margaret Boden, Ulric Neisser, Rom Harre, Merlin Donald, among others, have all written chapters in a non-technical style that can be enjoyed and understood by an inter-disciplinary audience of psychologists, philosophers, anthropologists, linguists, and cognitive scientists alike.
Although there has been much progress in developing theories, models and systems in the areas of Natural Language Processing (NLP) and Vision Processing (VP), there has heretofore been little progress on integrating these two subareas of Artificial Intelligence (AI). This book contains a set of edited papers addressing theoretical issues and the grounding of representations in NLP and VP from philosophical and psychological points of view. The papers focus on site descriptions such as the reasoning work on space at Leeds, UK, the systems work of the ILS (Illinois, U.S.A.) and philosophical work on grounding at Torino, Italy, on Schank's earlier work on pragmatics and meaning incorporated into hypermedia teaching systems, Wilks' visions on metaphor, on experimental data for how people fuse language and vision and theories and computational models, mainly connectionist, for tackling Searle's Chinese Room Problem and Harnad's Symbol Grounding Problem. The Irish Room is introduced as a mechanism through which integration solves the Chinese Room. The U.S.A., China and the EU are well reflected, showing the fact that integration is a truly international issue. There is no doubt that all of this will be necessary for the SuperInformationHighways of the future.
