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Titlebook: Computational Intelligence in Games; Norio Baba,Lakhmi C. Jain Book 2001 Springer-Verlag Berlin Heidelberg 2001 algorithms.computational i

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發(fā)表于 2025-3-21 16:38:05 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computational Intelligence in Games
編輯Norio Baba,Lakhmi C. Jain
視頻videohttp://file.papertrans.cn/233/232501/232501.mp4
概述Presentation of state-of-the-art concepts and most recent research on the application of computational intelligence techniques in games.Including a new approach of evolving a neural network to play ch
叢書名稱Studies in Fuzziness and Soft Computing
圖書封面Titlebook: Computational Intelligence in Games;  Norio Baba,Lakhmi C. Jain Book 2001 Springer-Verlag Berlin Heidelberg 2001 algorithms.computational i
描述The most powerful computers in the world are not only used for scientific research, defence, and business, but also in game playing. Computer games are a multi-billion dollar industry. Recent advances in computational intelligence paradigms have generated tremendous interest among researchers in the theory and implementation of games. Game theory is a branch of operational research dealing with decision theory in a competitive situation. Game theory involves the mathematical calculations and heuristics to optimize the efficient lines of play. This book presents a sample of the most recent research on the application of computational intelligence techniques in games. This book contains 7 chapters. The first chapter, by Chen, Fanelli, Castellano, and Jain, is an introduction to computational intelligence paradigms. It presents the basics of the main constituents of compu- tational intelligence paradigms including knowledge representation, probability-based approaches, fuzzy logic, neural networks, genetic algorithms, and rough sets. In the second chapter, Chellapilla and Fogel present the evolution of a neural network to play checkers without human expertise. This chapter focuses on
出版日期Book 2001
關(guān)鍵詞algorithms; computational intelligence; fuzzy; fuzzy logic; game theory; genetic algorithms; heuristics; in
版次1
doihttps://doi.org/10.1007/978-3-7908-1833-8
isbn_softcover978-3-662-00369-5
isbn_ebook978-3-7908-1833-8Series ISSN 1434-9922 Series E-ISSN 1860-0808
issn_series 1434-9922
copyrightSpringer-Verlag Berlin Heidelberg 2001
The information of publication is updating

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Adam Sobanski Dr.,Jens Hollmanntise in the form of features that are believed to be important to playing well. In particular, we have focused on the use of a population of neural networks, where each network serves as an evaluation function to describe the quality of the current board position. After only a little more than 800 g
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Jens Hollmann,Adam Sobanski Dr.at make position evaluation extremely difficult. Development of conventional Go programs is hampered by their knowledge-intensive nature. We demonstrate a viable alternative by training neural networks to evaluate Go positions via temporal difference (TD) learning.
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https://doi.org/10.1007/978-3-8349-8591-0markets (e.g., five markets). Our market selection game is a non-cooperative repeated game where every player is supposed to simultaneously choose a single market for maximizing its own payoff obtained by selling its product at the selected market. It is assumed that the market price of the product
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Norio Baba,Lakhmi C. JainPresentation of state-of-the-art concepts and most recent research on the application of computational intelligence techniques in games.Including a new approach of evolving a neural network to play ch
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