Understanding mario

Abstract

Evaluating the output of content generators is still one of the key open research challenges in Procedural Content Generation (PCG). This paper presents a collection of metrics for evaluating the quality of platform game levels, and analyzes how well these metrics are able to capture the human-perceived difficulty, visual aesthetics and enjoyment of these levels. We show empirically, in the context of Infinite Mario Bros (IMB), that some of the proposed metrics yield correlation values with human ratings that are near empirical upper bounds derived from a human inter-rater agreement study. We also show that a simple linear regression model using a subset of our metrics as input features is able to substantially outperform a previous approach that uses a neural network for predicting human-perceived difficulty, visual aesthetics, and enjoyment in IMB levels.