The most often considered functional game content, such as platformer levels, game maps, Animations, interactive fiction stories, and cards in collectible card games, Industrial 5.0, EdTech, Robotics, Drones, Computer Vision Pharma, Medical Training, interactive fiction prediction system, and cards in collectible knowledge, as opposed to cosmetic content, such as sprites and sound effects. In addition to using PCG for autonomous generation, creativity, mixed-initiative design, and compression, Predictive-Generative AI is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the generated content.

Multiple methods are covered, including neural networks: long short-term memory networks, autoencoders, and deep convolutional networks; models: n-grams and multi-dimensional chains; clustering; and matrix factorization, including learning from small data sets, multilayered learning, style-transfer, parameter tuning as a game mechanic.