| 书目名称 | Learning and Coordination | | 副标题 | Enhancing Agent Perf | | 编辑 | Steven H. Kim | | 视频video | | | 丛书名称 | Intelligent Systems, Control and Automation: Science and Engineering | | 图书封面 |  | | 描述 | Intelligent systems of the natural kind are adaptive androbust: they learn over time and degrade gracefully under stress. Ifartificial systems are to display a similar level of sophistication,an organizing framework and operating principles are required tomanage the resulting complexity of design and behavior. .This book presents a general framework for adaptive systems. Theutility of the comprehensive framework is demonstrated by tailoring itto particular models of computational learning, ranging from neuralnetworks to declarative logic. .The key to robustness lies in distributed decision making. An exemplarof this strategy is the neural network in both its biological andsynthetic forms. In a neural network, the knowledge is encoded in thecollection of cells and their linkages, rather than in any singlecomponent. Distributed decision making is even more apparent in thecase of independent agents. For a population of autonomous agents,their proper coordination may well be more instrumental for attainingtheir objectives than are their individual capabilities. .This book probes the problems and opportunities arising fromautonomous agents acting individually and collectively. Following | | 出版日期 | Book 1994 | | 关键词 | agents; autonomous agent; autonomous agents; behavior; complexity; decision making; design; intelligent sys | | 版次 | 1 | | doi | https://doi.org/10.1007/978-94-011-1016-7 | | isbn_softcover | 978-94-010-4442-4 | | isbn_ebook | 978-94-011-1016-7Series ISSN 2213-8986 Series E-ISSN 2213-8994 | | issn_series | 2213-8986 | | copyright | Springer Science+Business Media Dordrecht 1994 |
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发表于 2025-3-21 19:02:21
