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Titlebook: Computer Games; 5th Workshop on Comp Tristan Cazenave,Mark H.M. Winands,Julian Togelius Conference proceedings 2017 Springer International

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书目名称Computer Games
副标题5th Workshop on Comp
编辑Tristan Cazenave,Mark H.M. Winands,Julian Togelius
视频video
概述Includes supplementary material: .Includes supplementary material:
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Computer Games; 5th Workshop on Comp Tristan Cazenave,Mark H.M. Winands,Julian Togelius Conference proceedings 2017 Springer International
描述This book constitutes the refereed proceedings of the 5th Computer Games Workshop, CGW 2016, and the 5th Workshop on General Intelligence in Game-Playing Agents, GIGA 2016, held in conjunction with the 25th International Conference on Artificial Intelligence, IJCAI 2016, in New York, USA, in July 2016.The 12 revised full papers presented were carefully reviewed and selected from 25 submissions. The papers address all aspects of artificial intelligence and computer game playing. They discuss topics such as Monte-Carlo methods; heuristic search; board games; card games; video games; perfect and imperfect information games; puzzles and single player games; multi-player games; combinatorial game theory; applications; computational creativity; computational game theory; evaluation and analysis; game design; knowledge representation; machine learning; multi-agent systems; opponent modeling; planning. .
出版日期Conference proceedings 2017
关键词algorithms; agents; artificial intelligence; computer games; game tree search; language; machine learning;
版次1
doihttps://doi.org/10.1007/978-3-319-57969-6
isbn_softcover978-3-319-57968-9
isbn_ebook978-3-319-57969-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Matching Techniques, Algorithms, and Systemsiction system of Latent Factor Ranking (LFR). In this paper, we investigate the problem of integrating feature knowledge learned by FBT model in Monte Carlo Tree Search. We use the open source Go program Fuego as the test platform. Experimental results show that the FBT knowledge is useful in improv
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Matching Evaluations and Datasetsed Rollout Policy Adaptation (NRPA) is an MCTS variant that has found record-breaking solutions for puzzles and optimization problems. It learns a playout policy online that dynamically adapts the playouts to the problem at hand. We propose to enhance NRPA using more selectivity in the playouts. The
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Matching Techniques, Algorithms, and Systemstari 2600. We consider four models of neural networks which differ in size and architecture: two networks which use only information contained in the RAM and two mixed networks which use both information in the RAM and information from the screen..As the benchmark we used the convolutional model pro
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https://doi.org/10.1007/978-981-19-8645-1understanding of which aspects of game states influence game-play. This paper presents a clustering and locally weighted regression method for modeling and imitating individual players. The algorithm first learns a generic player cluster model that is updated online to capture an individual’s game-p
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