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Titlebook: Procedural Content Generation via Machine Learning; An Overview Matthew Guzdial,Sam Snodgrass,Adam J. Summerville Book 2022 The Editor(s) (

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書目名稱Procedural Content Generation via Machine Learning
副標(biāo)題An Overview
編輯Matthew Guzdial,Sam Snodgrass,Adam J. Summerville
視頻videohttp://file.papertrans.cn/758/757346/757346.mp4
概述Addresses the growing academic interest in PCGML.Demonstrates common pitfalls in PCGML projects and how to avoid them.Provides resources and guidance for starting new PCGML projects
叢書名稱Synthesis Lectures on Games and Computational Intelligence
圖書封面Titlebook: Procedural Content Generation via Machine Learning; An Overview Matthew Guzdial,Sam Snodgrass,Adam J. Summerville Book 2022 The Editor(s) (
描述This book surveys current and future approaches to generating video game content with machine learning or Procedural Content Generation via Machine Learning (PCGML). ?Machine learning is having a major impact on many industries, including the video game industry.? PCGML addresses the use of computers to generate new types of content for video games (game levels, quests, characters, etc.) by learning from existing content.? The authors illustrate how PCGML is poised to transform the video games industry and provide the first ever beginner-focused guide to PCGML.? This book features an accessible introduction to machine learning topics, and readers will gain a broad understanding of currently employed PCGML approaches in academia and industry.? The authors provide guidance on how best to set up a PCGML project and identify open problems appropriate for a research project or thesis.? This book is written with machine learning and games novices in mind and includes discussions of practical and ethical considerations along with resources and guidance for starting a new PCGML project..
出版日期Book 2022
關(guān)鍵詞Procedural Content Generation; Machine Learning; Artificial Intelligence; Video Games; Game Design; Compu
版次1
doihttps://doi.org/10.1007/978-3-031-16719-5
isbn_softcover978-3-031-16721-8
isbn_ebook978-3-031-16719-5Series ISSN 2573-6485 Series E-ISSN 2573-6493
issn_series 2573-6485
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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