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Procedural Content Generation via Machine Learning

By Matthew Guzdial

Procedural Content Generation via Machine Learning Computer Science

Procedural Content Generation via Machine Learning

By Matthew Guzdial

0 - Default Title
Description
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. The authors survey current and future approaches to generating video game content and illustrate the major impact that PCGML has had on video games industry. In order to provide the most up-to-date information, this new edition incorporates the last two years of research and advancements in this rapidly developing area. The book guides readers on how best to set up a PCGML project and identify open problems appropriate for a research project or thesis. The authors discuss the practical and ethical considerations for MCGML projects and demonstrate how to avoid the common pitfalls. This second edition also introduces a new chapter on Generative AI, which covers the benefits, risks, and methods for applying pre-trained transformers to PCG problems. In addition, this book; - Provides readers with a broad understanding of currently employed PCGML approaches in academia and industry. - Presents resources and guidance for starting a new PCGML project with machine learning and games novices in mind. - Highlights current open problems and areas for future study based on the latest research and industrial advancements.
Product details
Edition:
2
Number of Pages:
312
Release Date:
2025-05-31
Publication Date:
2025-05-31
Publisher:
Springer Nature Switzerland
Languages:
Original: English
ISBN10:
3031847555
ISBN13:
9783031847554
GPSR Manufacturer Reference:
[email protected]
Weight:
700 g
Height:
173 cm
Width:
246 cm
Thickness:
23 cm
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