Automatic Generation of Super Mario Levels via Graph Grammars
Eduardo Hauck, Claus C. Aranha · 2020 IEEE Conference on Games (CoG) · 2020
Automatically generating game levels using Procedural Content Generation (PCG) is a challenging problem because of the necessity of attending both functional and nonfunctional requirements. In the particular example of level generation for Super Mario, Machine Learning approaches, such as GANs and Reinforcement Learning, have shown promise. However, these black-box approaches usually are not explainable, and thus difficult to integrate with human designers. We propose a different level-generation system that uses a reachable graph structure, automatic detection of level structures, and formal graph grammars. A human designer can also easily add or remove structures to use the system as a level co-creation tool. An experimental analysis shows that the proposed system can generate playable and visually pleasing levels, while revealing some limitations of current approaches on platformer level generation, such as the generation of backtracking segments.