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Learning and Coordination: Enhancing Agent Performance Through Distributed Decision Making: 13 - Rilegato
Sinossi
Intelligent systems of the natural kind are adaptive and robust: they learn over time and degrade gracefully under stress. If artificial systems are to display a similar level of sophistication, an organizing framework and operating principles are required to manage the resulting complexity of design and behavior.
This book presents a general framework for adaptive systems. The utility of the comprehensive framework is demonstrated by tailoring it to particular models of computational learning, ranging from neural networks to declarative logic.
The key to robustness lies in distributed decision making. An exemplar of this strategy is the neural network in both its biological and synthetic forms. In a neural network, the knowledge is encoded in the collection of cells and their linkages, rather than in any single component. Distributed decision making is even more apparent in the case of independent agents. For a population of autonomous agents, their proper coordination may well be more instrumental for attaining their objectives than are their individual capabilities.
This book probes the problems and opportunities arising from autonomous agents acting individually and collectively. Following the general framework for learning systems and its application to neural networks, the coordination of independent agents through game theory is explored. Finally, the utility of game theory for artificial agents is revealed through a case study in robotic coordination.
Given the universality of the subjects -- learning behavior and coordinative strategies in uncertain environments -- this book will be of interest to students and researchers in various disciplines, ranging from all areas of engineering to the computing disciplines; from the life sciences to the physical sciences; and from the management arts to social studies.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Contenuti
Preface. 1. Introduction and Framework. 2. Learning Speed in Neural Networks. 3. Principles of Coordination. 4. Case Study in Coordination. 5. Conclusion. Appendix: Dynamic Models in Statistical Physics. Index.
Product Description
Book by Kim SH
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- EditoreKluwer Academic Pub
- Data di pubblicazione1994
- ISBN 10 0792330463
- ISBN 13 9780792330462
- RilegaturaCopertina rigida
- LinguaInglese
- Numero di pagine204
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Learning and Coordination: Enhancing Agent Performance through Distributed Decision Making (Intelligent Systems, Control and Automation: Science and Engineering, 13)
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Condizione: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, and may have sticker on cover, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions. Codice articolo Z1-G-012-01557
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Learning and Coordination: Enhancing Agent Performance through Distributed Decision Making (Intelligent Systems, Control and Automation: Science and Engineering, 13)
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Learning and Coordination: Enhancing Agent Performance through Distributed Decision Making (Intelligent Systems, Control and Automation: Science and Engineering, 13)
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Learning and Coordination
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Intelligent systems of the natural kind are adaptive and robust: they learn over time and degrade gracefully under stress. If artificial systems are to display a similar level of sophistication, an organizing framework and operating principles are requir. Codice articolo 5967215
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Learning and Coordination : Enhancing Agent Performance through Distributed Decision Making
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Springer Netherlands, Springer Netherlands, 1994
ISBN 10: 0792330463
ISBN 13: 9780792330462
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Intelligent systems of the natural kind are adaptive and robust: they learn over time and degrade gracefully under stress. If artificial systems are to display a similar level of sophistication, an organizing framework and operating principles are required to manage the resulting complexity of design and behavior. This book presents a general framework for adaptive systems. The utility of the comprehensive framework is demonstrated by tailoring it to particular models of computational learning, ranging from neural networks to declarative logic. The key to robustness lies in distributed decision making. An exemplar of this strategy is the neural network in both its biological and synthetic forms. In a neural network, the knowledge is encoded in the collection of cells and their linkages, rather than in any single component. Distributed decision making is even more apparent in the case of independent agents. For a population of autonomous agents, their proper coordination may well be more instrumental for attaining their objectives than are their individual capabilities. This book probes the problems and opportunities arising from autonomous agents acting individually and collectively. Following the general framework for learning systems and its application to neural networks, the coordination of independent agents through game theory is explored. Finally, the utility of game theory for artificial agents is revealed through a case study in robotic coordination. Given the universality of the subjects -- learning behavior and coordinative strategies in uncertain environments -- this book will be of interest to students and researchers in various disciplines, ranging from all areas of engineering to the computing disciplines; from the life sciences to the physical sciences; and from the management arts to social studies. Codice articolo 9780792330462
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Learning and Coordination
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Springer Netherlands Sep 1994, 1994
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ISBN 13: 9780792330462
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Intelligent systems of the natural kind are adaptive and robust: they learn over time and degrade gracefully under stress. If artificial systems are to display a similar level of sophistication, an organizing framework and operating principles are required to manage the resulting complexity of design and behavior. This book presents a general framework for adaptive systems. The utility of the comprehensive framework is demonstrated by tailoring it to particular models of computational learning, ranging from neural networks to declarative logic. The key to robustness lies in distributed decision making. An exemplar of this strategy is the neural network in both its biological and synthetic forms. In a neural network, the knowledge is encoded in the collection of cells and their linkages, rather than in any single component. Distributed decision making is even more apparent in the case of independent agents. For a population of autonomous agents, their proper coordination may well be more instrumental for attaining their objectives than are their individual capabilities. This book probes the problems and opportunities arising from autonomous agents acting individually and collectively. Following the general framework for learning systems and its application to neural networks, the coordination of independent agents through game theory is explored. Finally, the utility of game theory for artificial agents is revealed through a case study in robotic coordination. Given the universality of the subjects -- learning behavior and coordinative strategies in uncertain environments -- this book will be of interest to students and researchers in various disciplines, ranging from all areas of engineering to the computing disciplines; from the life sciences to the physical sciences; and from the management arts to social studies. 204 pp. Englisch. Codice articolo 9780792330462
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Hardcover. Condizione: Brand New. 1st edition. 204 pages. 10.00x6.75x0.50 inches. In Stock. Codice articolo x-0792330463
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