Procedural Content Generation via Machine Learning
Produktnummer:
18c5023cb446004797a8abdab13ee8dd37
| Autor: | Guzdial, Matthew Snodgrass, Sam Summerville, Adam |
|---|---|
| Themengebiete: | Artificial Intelligence Computational Creativity Computer Science Game Design Generative AI Machine Learning PCGML Procedural Content Generation Video Games |
| Veröffentlichungsdatum: | 09.07.2025 |
| EAN: | 9783031847561 |
| Auflage: | 2 |
| Sprache: | Englisch |
| Seitenzahl: | 295 |
| Produktart: | Unbekannt |
| Verlag: | Springer International Publishing |
| Untertitel: | An Overview |
Produktinformationen "Procedural Content Generation via Machine Learning"
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 PCGML 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.
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