AI-Evaluated Procedural Content Generation for Video Game Level Design

  • Daniel Millward University of Derby

Abstract

Procedural Content Generation (PCG) has long been used in video games to produce content that would otherwise be restricted by time, cost, or memory limitations. A relatively new area of research within the field is that of Search-Based PCG, which uses a process inspired by biological evolution to evolve content over iterative generations. Expanding on this work, we present a prototype of AI-Evaluated PCG, which aims to generate content with as few human-authored rules as possible, by using an AI-driven Bot for content evaluation. Our working prototype focuses on level generation for a 2D platforming game similar to Nintendo's Super Mario Bros. We develop a bot which, through the use of an artificial neural network, learns from player behaviour across a number of training sessions, and replicates that behaviour to test-run and evaluate generated levels. Level evaluation is used to score potential level solutions, and a genetic algorithm employed to evolve our level content. Ultimately our prototype is able to successfully generate complete and playable levels, with room for improvement. We detail a list of recommendations for further research.
Published
2017-06-07
How to Cite
MILLWARD, Daniel. AI-Evaluated Procedural Content Generation for Video Game Level Design. Discovery, Invention & Application, [S.l.], june 2017. Available at: <https://computing.derby.ac.uk/ojs/index.php/da/article/view/245>. Date accessed: 18 mar. 2019.
Issue
Section
Articles