Improving Deep Localized Level Analysis: How Game Logs Can Help
This work addresses player experience prediction for game developers, but it is incremental as it builds on existing methods with specific data enhancements.
The paper tackled affect prediction in player modeling by using a deep CNN trained on game event logs and localized level structure, achieving improved performance over prior work on Super Mario Bros.-based levels.
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.