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This is the first comprehensive introduction to multiagent systems and contemporary distributed artificial intelligence that is suitable as a textbook. The book provides detailed coverage of basic topics as well as several closely related ones.Unlike traditional textbooks, the book brings together many leading experts, guaranteeing a broad and diverse base of knowledge and expertise. It emphasizes aspects of both theory and application, and provides many illustrations and examples. Also included are thought-provoking exercises of varying degrees of difficulty and a twenty-page glossary of terms found in the study of agents, multiagent systems, and distributed artificial intelligence.The book can be used for teaching as well as self-study, and is designed to meet the needs of both researchers and practitioners. In view of the interdisciplinary nature of the field, it will be a useful reference not only for computer scientists and engineers, but for social scientists and management and organization scientists as well.Contributors: Gul A. Agha, Kathleen M. Carley, Jose Cuena, Edmund H. Durfee, Clarence Ellis, Les Gasser, Michael P. Georgeff, Michael N. Huhns, Toru Ishida, Nadeem Jamali, Sascha Ossowski, H. Van Dyke Parunak, Anand S. Rao, Tuomas W. Sandholm, Sandip Sen, Munindar P. Singh, Larry M. Stephens, Gerard Tel, Jacques Wainer, Gerhard Weiss, Michael J. Wooldridge, Makoto Yokoo.

This is the first comprehensive introduction to multiagent systems and contemporary distributed artificial intelligence that is suitable as a textbook. The book provides detailed coverage of basic topics as well as several closely related ones. Unlike traditional textbooks, the book brings together many leading experts, guaranteeing a broad and diverse base of knowledge and expertise. It emphasizes aspects of both theory and application, and provides many illustrations and examples. Also included are thought-provoking exercises of varying degrees of difficulty and a twenty-page glossary of terms found in the study of agents, multiagent systems, and distributed artificial intelligence. The book can be used for teaching as well as self-study, and is designed to meet the needs of both researchers and practitioners. In view of the interdisciplinary nature of the field, it will be a useful reference not only for computer scientists and engineers, but for social scientists and management and organization scientists as well. Contributors: Gul A. Agha, Kathleen M. Carley, Jose Cuena, Edmund H. Durfee, Clarence Ellis, Les Gasser, Michael P. Georgeff, Michael N. Huhns, Toru Ishida, Nadeem Jamali, Sascha Ossowski, H. Van Dyke Parunak, Anand S. Rao, Tuomas W. Sandholm, Sandip Sen, Munindar P. Singh, Larry M. Stephens, Gerard Tel, Jacques Wainer, Gerhard Weiss, Michael J. Wooldridge, Makoto Yokoo.

This is the first comprehensive introduction to multiagent systems and contemporary distributed artificial intelligence that is suitable as a textbook. The book provides detailed coverage of basic topics as well as several closely related ones. Unlike traditional textbooks, the book brings together many leading experts, guaranteeing a broad and diverse base of knowledge and expertise. It emphasizes aspects of both theory and application, and provides many illustrations and examples. Also included are thought-provoking exercises of varying degrees of difficulty and a twenty-page glossary of terms found in the study of agents, multiagent systems, and distributed artificial intelligence. The book can be used for teaching as well as self-study, and is designed to meet the needs of both researchers and practitioners. In view of the interdisciplinary nature of the field, it will be a useful reference not only for computer scientists and engineers, but for social scientists and management and organization scientists as well. Contributors: Gul A. Agha, Kathleen M. Carley, Jose Cuena, Edmund H. Durfee, Clarence Ellis, Les Gasser, Michael P. Georgeff, Michael N. Huhns, Toru Ishida, Nadeem Jamali, Sascha Ossowski, H. Van Dyke Parunak, Anand S. Rao, Tuomas W. Sandholm, Sandip Sen, Munindar P. Singh, Larry M. Stephens, Gerard Tel, Jacques Wainer, Gerhard Weiss, Michael J. Wooldridge, Makoto Yokoo.

The transition from sequential to parallel computation is an area of critical concern in today's computer technology, particularly in architecture, programming languages, systems, and artificial intelligence. This book addresses central issues in concurrency, and by producing both a syntactic definition and a denotational model of Hewitt's actor paradigm―a model of computation specifically aimed at constructing and analyzing distributed large-scale parallel systems―it substantially advances the understanding of parallel computation. ContentsIntroduction · General Design Decisions · Computation in ACTOR Systems · A More Expressive Language · A Model for ACTOR Systems · Concurrency Issues · Abstraction and Compositionality · Conclusions

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Artificial Intelligence DJ Haus - LP 800071004873

The Artificial Intelligence Robot STEM Kit lets you build an intelligent, six-legged, app-enabled robot that uses machine learning to react to your gestures and sounds. Download the free app for your mobile device to record the voice commands and gestures you'll use to make it walk, turn and stop. The more data you collect, the better the AI will learn from it. App compatible with iPhone/iPad (iOS 11.0 or later) and Galaxy/Android (OS 8.0 or later). Ages 10+.

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A leading contributor to artificial intelligence offers insight into the numerous ways in which the mind works to demonstrate how emotions and feelings are just different ways of thinking, in an account that poses controversial ideas about the potential for designing machines that are capable of thinking like humans. By the author of The Society of Mind.

New! This book is in the same immaculate condition as when it was published 1.05

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These twenty-three contributions focus on the most recent developments in the rapidly evolving field of geometric invariants and their application to computer vision. The introduction summarizes the basics of invariant theory, discusses how invariants are related to problems in computer vision, and looks at the future possibilities, particularly the notion that invariant analysis might provide a solution to the elusive problem of recognizing general curved 3D objects from an arbitrary viewpoint. The remaining chapters consist of original papers that present important developments as well as tutorial articles that provide useful background material. These chapters are grouped into categories covering algebraic invariants, nonalgebraic invariants, invariants of multiple views, and applications. An appendix provides an extensive introduction to projective geometry and its applications to basic problems in computer vision.

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