# Improving Deep Localized Level Analysis: How Game Logs Can Help

arXiv: https://arxiv.org/abs/2212.03376 · HTML: https://mlsanity.com/paper/2212.03376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [The VGLC: The Video Game Level Corpus](https://mlsanity.com/paper/1606.07487/) (2016)
- [Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study](https://mlsanity.com/paper/2106.15877/) (2021)
- [Towards General Models of Player Experience: A Study Within Genres](https://mlsanity.com/paper/2110.00978/) (2021)
- [Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data](https://mlsanity.com/paper/1802.02301/) (2018)
- [Player Experience Extraction from Gameplay Video](https://mlsanity.com/paper/1809.06201/) (2018)
- [Making CNNs for Video Parsing Accessible](https://mlsanity.com/paper/1906.11877/) (2019)
- [Evaluating Singleplayer and Multiplayer in Human Computation Games](https://mlsanity.com/paper/1703.00818/) (2017)

## Abstract (arXiv)

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.