Procedural content generation is the use of algorithms (procedures) to create novel, and sometimes customized, game content from scratch. Examples of PCG include generation of levels, maps, tree, cityscapes, weapons, monsters, and quests. PCG is often used as a design-time tool to roughly sketch out level content to be refined by human designers. PCG can also be done at run-time to incorporate individual player differences such as skills or preferences. In this project, I look at run-time PCG to create Mario Bros. game levels customized to individual players’ play styles. This includes (a) learning a model of the player’s play style, and (b) using the model to create a custom level. I must focus on designing and implementing algorithms that use the player information to create something that will evaluate well.
This project will be using the 2011 IEEE Super Mario Bros. Competition infrastructure (Level Generation Track). I will write a procedural content generator in the provided Mario Bros. game engine that optimizes level content for different types of players such as those who like to jump, like to collect coins, or like to kill enemies. I will implement a genetic algorithm to tune the layout of the Mario Bros. level.
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