| 书目名称 | Procedural Content Generation via Machine Learning |
| 副标题 | An Overview |
| 编辑 | Matthew Guzdial,Sam Snodgrass,Adam J. Summerville |
| 视频video | |
| 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 |
| 丛书名称 | Synthesis Lectures on Games and Computational Intelligence |
| 图书封面 |  |
| 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 |
| 关键词 | Procedural Content Generation; Machine Learning; Artificial Intelligence; Video Games; Game Design; Compu |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-16719-5 |
| isbn_softcover | 978-3-031-16721-8 |
| isbn_ebook | 978-3-031-16719-5Series ISSN 2573-6485 Series E-ISSN 2573-6493 |
| issn_series | 2573-6485 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 17:27:58
