Toward Game Level Generation from Gameplay Videos
This addresses the need for automated game content generation for game developers, but it is incremental as it builds on existing methods for parsing and modeling.
The authors tackled the problem of generating game levels by automatically learning design knowledge from gameplay videos, using Super Mario Bros. as a proof of concept, and achieved results measured by playability and stylistic similarity to original levels.
Algorithms that generate computer game content require game design knowledge. We present an approach to automatically learn game design knowledge for level design from gameplay videos. We further demonstrate how the acquired design knowledge can be used to generate sections of game levels. Our approach involves parsing video of people playing a game to detect the appearance of patterns of sprites and utilizing machine learning to build a probabilistic model of sprite placement. We show how rich game design information can be automatically parsed from gameplay videos and represented as a set of generative probabilistic models. We use Super Mario Bros. as a proof of concept. We evaluate our approach on a measure of playability and stylistic similarity to the original levels as represented in the gameplay videos.