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Название: Procedural Content Generation Via Machine Learning: An Overview, 2nd EditionАвтор: Matthew Guzdial, Sam Snodgrass, Adam Summerville
Издательство: Springer
Год: 2025
Страниц: 300
Язык: английский
Формат: pdf (true), epub
Размер: 38.5 MB
This second edition updates and expands upon the first beginner-focused guide to Procedural Content Generation via Machine Learning (PCGML), which is the use of computers to generate new types of content for video games (game levels, quests, characters, etc.) by learning from existing content. By game content, we mean the various component parts of a game, including scripts or game code, levels or maps, character sprites or 3D models, animations, music, sound effects, and so on. By algorithmic generation we indicate some process or set of rules rather than typical human creation. Most often “algorithmic” indicates the use of computer code, and that’s the way we’ll discuss it in this book, but it could involve any set process or rules, such as generation via cards or dice. Our hope is that this book is accessible to PCG practitioners, ML practitioners, and anyone interested in these topics. The book can be used as the basis for a class, with every chapter serving as the basis of 1–2 lectures, as an introduction to these topics, or simply as a reference or guide. Our hope is that this book can demystify ML for those on the game design and PCG side of things, and make the benefits of applying ML to PCG clear for those on the ML side of things. For a class, we have written this book to be programming language agnostic, but it will require at least some understanding of coding.
