Evaluating the Expressive Range of Super Mario Bros Level Generators

Abstract

Procedural Content Generation for video games (PCG) is widely used by today’s video game industry to create huge open worlds or enhance replayability. However, there is little scientific evidence that these systems produce high-quality content. In this document, we evaluate three open-source automated level generators for Super Mario Bros in addition to the original levels used for training. These are based on Genetic Algorithms, Generative Adversarial Networks, and Markov Chains. The evaluation was performed through an Expressive Range Analysis (ERA) on 200 levels with nine metrics. The results show how analyzing the algorithms’ expressive range can help us evaluate the generators as a preliminary measure to study whether they respond to users’ needs. This method allows us to recognize potential problems early in the content generation process, in addition to taking action to guarantee quality content when a generator is used.

Keywords

• Procedural Content Generation
• evaluation methods
• Expressive Range Analysis
• platformer
• videogames