Procedural Content Generation via Machine Learning An Overview / [electronic resource] :
by Matthew Guzdial, Sam Snodgrass, Adam J. Summerville.
- 1st ed. 2022.
- XIII, 238 p. 82 illus., 63 illus. in color. online resource.
- Synthesis Lectures on Games and Computational Intelligence, 2573-6493 .
- Synthesis Lectures on Games and Computational Intelligence, .
Introduction -- Classical PCG -- An Introduction of 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 -- Mixed-Initiative PCGML -- Open Problems -- Resource and Conclusions.
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.