Improving Deep Localized Level Analysis: How Game Logs Can Help
In this EXAG 2022 presentation, Natalie Bombardieri discusses improving deep localized level analysis. Player modelling is the field of study associated with understanding players. One pursuit in this field is affect prediction: the ability to predict how a game will make a player feel. We present novel improvements to affect prediction by using a deep convolutional neural network (CNN) to predict player experience trained on raw game event logs in tandem with localized level structure information. We test our approach on levels based on Super Mario Bros, Infinite Mario Bros, and Super Mario Bros.: The Lost Levels, Gwario, as well as original Super Mario Bros. levels without retraining. We outperform prior work, and demonstrate the importance of training on player logs, even when lacking them at test time.