Loose Cannon (chap book #7), Shadows and Shades (chap book #8), Quiet Knives (chap book #9), With Stars Underfoot (chap book #10), Necessary Evils (chap book #11), and Allies (chap book #12).

    It involves building a computational model consisting of agents, each of which represents an actor in the social world, and an environment in which the agents act. Agents are able to interact with each other and are programmed to be pro-active, autonomous and able to perceive their virtual world. The techniques of ABM are derived from artificial intelligence and computer science, but are now being developed independently in research centers throughout the world.In Agent-Based Models, Nigel Gilbert reviews a range of examples of agent-based modeling, describes how to design and build your own models, and considers practical issues such as verification, validation, planning a modeling project, and how to structure a scholarly article reporting the results of agent-based modeling. It includes a glossary, an annotated list of resources, advice on which programming environment to use when creating agent-based models, and a worked, step-by-step example of the development of an ABM.This latest volume in the SAGE Quantitative Applications in the Social Sciences series will have wide appeal in the social sciences, including the disciplines of sociology, economics, social psychology, geography, economic history, science studies, and environmental studies. It is appropriate for graduate students, researchers and academics in these fields, for both those wanting to keep up with new developments in their fields and those who are considering using ABM for their research.Key FeaturesAimed at readers who are new to ABMOffers a brief, but thorough, treatment of a cutting-edge techniqueOffers practical advice about how to design and create ABMIncludes carefully chosen examples from different disciplinesLearn more about The Little Green Book - QASS Series! Click Here

    In this book, the working cycle of a GA is explained in detail. It discusses the mechanisms of some specialized Gas with examples.

    The FastSLAM-type algorithms have enabled robots to acquire maps of unprecedented size and accuracy, in a number of robot application domains and have been successfully applied in different dynamic environments, including a solution to the problem of people tracking.

    There are cases where the human computer, slow as it is, is faster than any artificial intelligence system. Are we faster because of the way we perceive knowledge as opposed to the way we represent it? The authors address this question by presenting neural network models that integrate the two most fundamental phenomena of cognition: our ability to learn from experience, and our ability to reason from what has been learned. This book is the first to offer a self-contained presentation of neural network models for a number of computer science logics, including modal, temporal, and epistemic logics. By using a graphical presentation, it explains neural networks through a sound neural-symbolic integration methodology, and it focuses on the benefits of integrating effective robust learning with expressive reasoning capabilities. The book will be invaluable reading for academic researchers, graduate students, and senior undergraduates in computer science, artificial intelligence, machine learning, cognitive science and engineering. It will also be of interest to computational logicians, and professional specialists on applications of cognitive, hybrid and artificial intelligence systems.

    They are, however, frequently studied in isolation, using a range of models to understand them. This book brings a unified approach to understanding these three processes. It shows how these fundamental functions for cognitive neuroscience can be understood in a common and unifying computational neuroscience framework. This framework links empirical research on brain function from neurophysiology, function neuroimaging, and the effects of brain damage, to a description of how neural networks in the brain implement these functions using a set of common principles. The book describes the principles of operation of these networks, and how they could implement such important functions as memory, attention, and decision-making.

    The Vorstellen Police were formed to track down and neutralize these threats to society using whatever technology they can bring to bear.A Miracle of Science is a romantic comedy revolving around an unlikely pair of police officers and an even more unlikely criminal, set in this world of Science Gone Mad.

    Traditionally, scientists (and Robert Frost) have offered two bleak answers to this profound issue: fire or ice. In The Intelligent Universe, James Gardner envisions a third dramatic alternative--a final state of the cosmos in which a highly evolved form of group intelligence engineers a cosmic renewal, the birth of a new universe.

    The basic philosophy of the original version has been kept in the new edition. That is, the book covers the most essential and widely employed material in each area, particularly the material important for real-world applications. Our goal is not to cover every latest progress in the fields, nor to discuss every detail of various techniques that have been developed. New sections/subsections added in this edition are: Simulated Annealing (Section 3.7), Boltzmann Machines (Section 3.8) and Extended Fuzzy if-then Rules Tables (Sub-section 5.5.3). Also, numerous changes and typographical corrections have been made throughout the manuscript. The Preface to the first edition follows. General scope of the book Artificial intelligence (AI) as a field has undergone rapid growth in diversification and practicality. For the past few decades, the repertoire of AI techniques has evolved and expanded. Scores of newer fields have been added to the traditional symbolic AI. Symbolic AI covers areas such as knowledge-based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing. The newer fields include neural networks, genetic algorithms or evolutionary computing, fuzzy systems, rough set theory, and chaotic systems.