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Titlebook: Cellular Learning Automata: Theory and Applications; Reza Vafashoar,Hossein Morshedlou,Mohammad Reza Me Book 2021 The Editor(s) (if applic

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發(fā)表于 2025-3-21 19:28:17 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Cellular Learning Automata: Theory and Applications
編輯Reza Vafashoar,Hossein Morshedlou,Mohammad Reza Me
視頻videohttp://file.papertrans.cn/224/223037/223037.mp4
概述Presents recent advances and developments in cellular learning automata.Addresses key topics and issues regarding the models, theories, algorithms, and applications of cellular learning automata.Highl
叢書(shū)名稱Studies in Systems, Decision and Control
圖書(shū)封面Titlebook: Cellular Learning Automata: Theory and Applications; Reza Vafashoar,Hossein Morshedlou,Mohammad Reza Me Book 2021 The Editor(s) (if applic
描述This book highlights both theoretical and applied advances in cellular learning automata (CLA), a type of hybrid computational model that has been successfully employed in various areas to solve complex problems and to model, learn, or simulate complicated patterns of behavior. Owing to CLA’s parallel and learning abilities, it has proven to be quite effective in uncertain, time-varying, decentralized, and distributed environments.?.The book begins with a brief introduction to various CLA models, before focusing on recently developed CLA variants. In turn, the research areas related to CLA are addressed as bibliometric network analysis perspectives. The next part of the book presents CLA-based solutions to several computer science problems in e.g. static optimization, dynamic optimization, wireless networks, mesh networks, and cloud computing. Given its scope, the book is well suited for all researchers in the fields of artificial intelligence and reinforcement learning.??.
出版日期Book 2021
關(guān)鍵詞Reinforcement Learning; Learning Automata; Cellular Learning Automata; Wavefront Cellular Learning Aut
版次1
doihttps://doi.org/10.1007/978-3-030-53141-6
isbn_softcover978-3-030-53143-0
isbn_ebook978-3-030-53141-6Series ISSN 2198-4182 Series E-ISSN 2198-4190
issn_series 2198-4182
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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https://doi.org/10.1007/978-3-531-92782-4CA) and learning automata (LA). Since CLA has both the computational power of cellular automata and the learning ability of learning automata, it is a useful technique for modeling, controlling, and solving many real problems in the unknown, distributed, and decentralized environments. In this chapt
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Michael Stephan,Peter-Paul Grosste the applicability of multi-reinforcement models in channel assignment in multiple collision domains. Next, we propose a method based on multi-reinforcement cellular learning automata for distributed channel assignment in the mesh network.
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https://doi.org/10.1007/978-3-531-92865-4 the users who can benefit insurance coverage depends on the finances of service providers. A cellular learning automaton (CLA) based loss-sharing approach among service providers is considered in this chapter to increase the financial capability of each service provider. ICLA, as an irregular form
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https://doi.org/10.1007/978-3-531-92883-8 solutions for various problems in such systems. By introducing Reinforcement Learning (RL) as an efficient approach in game theory, increasing literature is concerned with the theoretical convergence of RL-based approaches towards Nash equilibrium. Many Q-learning based attempts and multi-agent rei
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