A Machine-Learned Framework for Automatic Content Generation, Evaluation, and Critique


DiGRA/FDG '16 - Abstract Proceedings of the First International Joint Conference of DiGRA and FDG
Dundee, Scotland: Digital Games Research Association and Society for the Advancement of the Science of Digital Games, August, 2016
Number: 2
Volume: 13
ISBN / ISNN: ISSN 2342-9666

The majority of procedural level research has relied on human authored rules and heuristics. I intend to develop an end-to-end system capable of examining play, understanding levels and generating new content of similar style and playability, and finally offering analysis and critique of levels. The current roadmap utilizes computer vision, causal modeling, and neural network systems. The system should allow a human to step in at any point and make whatever changes they wish and get all downstream benefits.