Guzdial M. Procedural Content Generation Via Machine Learning..Overview 2ed 2025

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Guzdial M. Procedural Content Generation Via Machine Learning..Overview 2ed 2025

Textbook in PDF format 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. Procedural Content Generation (PCG) refers to the algorithmic generation of game 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. PCG can look like the landscapes of Minecraft, the conversations and dungeons of Hades, or almost everything present in No Man’s Sky Preface Introduction Classical PCG An Introduction to ML Through PCG PCGML Process Overview Constraint-Based PCGML Approaches Probabilistic PCGML Approaches Neural Networks—Introduction Sequence-Based DNN PCGML Grid-Based DNN PCGML Reinforcement Learning PCG Generative AI Mixed-Initiative PCGML Open Problems Resources and Conclusions