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Titlebook: Collectives and the Design of Complex Systems; Kagan Tumer,David Wolpert Book 2004 Springer Science+Business Media New York 2004 algorithm

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書目名稱Collectives and the Design of Complex Systems
編輯Kagan Tumer,David Wolpert
視頻videohttp://file.papertrans.cn/230/229595/229595.mp4
概述First book to seriously introduce collectives and their potential applications in a single volume
圖書封面Titlebook: Collectives and the Design of Complex Systems;  Kagan Tumer,David Wolpert Book 2004 Springer Science+Business Media New York 2004 algorithm
描述Many complex systems found in nature can be viewed as function optimizers. In particular, they can be viewed as such optimizers of functions in extremely high- dimensional spaces. Given the difficulty of performing such high-dimensional op- timization with modern computers, there has been a lot of exploration of computa- tional algorithms that try to emulate those naturally-occurring function optimizers. Examples include simulated annealing (SA [15,18]), genetic algorithms (GAs) and evolutionary computation [2,3,9,11,20-22,24,28]. The ultimate goal of this work is an algorithm that can, for any provided high-dimensional function, come close to extremizing that function. Particularly desirable would be such an algorithm that works in an adaptive and robust manner, without any explicit knowledge of the form of the function being optimized. In particular, such an algorithm could be used for distributed adaptive control---one of the most important tasks engineers will face in the future, when the systems they design will be massively distributed and horribly messy congeries ofcomputational systems.
出版日期Book 2004
關(guān)鍵詞algorithm; algorithms; artificial intelligence; calculus; collective intelligence; complex system; complex
版次1
doihttps://doi.org/10.1007/978-1-4419-8909-3
isbn_softcover978-1-4612-6472-9
isbn_ebook978-1-4419-8909-3
copyrightSpringer Science+Business Media New York 2004
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沙發(fā)
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板凳
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Managing Catastrophic Changes in a Collective,he information concerning such large macroscopic changes becomes encoded in the microscopic dynamics. Our findings suggest that these large endogenous changes can be avoided either by pre-design of the collective machinery itself or in the postdesign stage via continual monitoring and occasional “vaccinations.”
地板
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Book 2004ely high- dimensional spaces. Given the difficulty of performing such high-dimensional op- timization with modern computers, there has been a lot of exploration of computa- tional algorithms that try to emulate those naturally-occurring function optimizers. Examples include simulated annealing (SA [
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https://doi.org/10.1007/978-3-319-23648-3e of system dynamics and because the computational cost of implementing particular equilibrium outcomes is also important. We discuss some of these challenges and provide a reinterpretation of the mathematics of collective intelligence in terms of . for bounded-rational agents.
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https://doi.org/10.1007/978-3-319-23648-3all we can do is to adjust the ways in which the agents interact with each other and the system in order to achieve our own design goals. In game theory, this is the mechanism design problem, and the design goal is denoted the “social choice function” (SCF). .
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viewed as function optimizers. In particular, they can be viewed as such optimizers of functions in extremely high- dimensional spaces. Given the difficulty of performing such high-dimensional op- timization with modern computers, there has been a lot of exploration of computa- tional algorithms th
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