Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content.
We propose and evaluate a new technique for learning hybrid automata automatically by observing the runtime behavior of a dynamical system.
While general game playing is an active field of research, the learning of game design has tended to be either a secondary goal of such research or it has been solely the domain of humans.
This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content.
The procedural generation of video game levels has existed for at least 30 years, but only recently have machine learning approaches been used to generate levels without specifying the rules for generation.