Improving Deep Localized Level Analysis: How Game Logs Can Help

• URL: http://arxiv.org/abs/2212.03376v1
• Date: Wed, 7 Dec 2022 00:05:16 GMT
• Title: Improving Deep Localized Level Analysis: How Game Logs Can Help
• Authors: Natalie Bombardieri, Matthew Guzdial
• Abstract summary: We present novel improvements to affect prediction by using a deep convolutional neural network (CNN) to predict player experience. We test our approach on levels based on Super Mario Bros. (Infinite Mario Bros.) and Super Mario Bros.: The Lost Levels (Gwario)
• Score: 0.9645196221785693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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. We outperform prior work, and demonstrate the utility of training on player logs, even when lacking them at test time for cross-domain player modelling.

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