# Analysis of Robustness of a Large Game Corpus

arXiv: https://arxiv.org/abs/2504.03940 · HTML: https://mlsanity.com/paper/2504.03940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 18 of 18)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Grammar Variational Autoencoder](https://mlsanity.com/paper/1703.01925/) (2017)
- [Parallelizing the dual revised simplex method](https://mlsanity.com/paper/1503.01889/) (2015)
- [Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network](https://mlsanity.com/paper/1805.00728/) (2018)
- [Level Generation Through Large Language Models](https://mlsanity.com/paper/2302.05817/) (2023)
- [An investigation of model-free planning](https://mlsanity.com/paper/1901.03559/) (2019)
- [Super Mario as a String: Platformer Level Generation Via LSTMs](https://mlsanity.com/paper/1603.00930/) (2016)
- [Bootstrapping Conditional GANs for Video Game Level Generation](https://mlsanity.com/paper/1910.01603/) (2019)
- [Learning to Generate Levels From Nothing](https://mlsanity.com/paper/2002.05259/) (2020)
- [When are Non-Parametric Methods Robust?](https://mlsanity.com/paper/2003.06121/) (2020)
- [Sequential Segment-based Level Generation and Blending using Variational Autoencoders](https://mlsanity.com/paper/2007.08746/) (2020)
- [Exploring Level Blending across Platformers via Paths and Affordances](https://mlsanity.com/paper/2009.06356/) (2020)
- [Intentional Computational Level Design](https://mlsanity.com/paper/1904.08972/) (2019)
- [Addressing the Fundamental Tension of PCGML with Discriminative Learning](https://mlsanity.com/paper/1809.04432/) (2018)
- [Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry](https://mlsanity.com/paper/2206.00106/) (2022)
- [Adversarial Random Forest Classifier for Automated Game Design](https://mlsanity.com/paper/2107.12501/) (2021)
- [Generating Lode Runner Levels by Learning Player Paths with LSTMs](https://mlsanity.com/paper/2107.12532/) (2021)
- [Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video Games](https://mlsanity.com/paper/2402.18728/) (2024)

## Built on by (most cited first, 1 of 1)

- [Representing and Generating Levels Over Time through Playtrace Reconstructive Partitioning](https://mlsanity.com/paper/2607.12097/) (2026)

## Abstract (arXiv)

Procedural content generation via machine learning (PCGML) in games involves using machine learning techniques to create game content such as maps and levels. 2D tile-based game levels have consistently served as a standard dataset for PCGML because they are a simplified version of game levels while maintaining the specific constraints typical of games, such as being solvable. In this work, we highlight the unique characteristics of game levels, including their structured discrete data nature, the local and global constraints inherent in the games, and the sensitivity of the game levels to small changes in input. We define the robustness of data as a measure of sensitivity to small changes in input that cause a change in output, and we use this measure to analyze and compare these levels to state-of-the-art machine learning datasets, showcasing the subtle differences in their nature. We also constructed a large dataset from four games inspired by popular classic tile-based games that showcase these characteristics and address the challenge of sparse data in PCGML by providing a significantly larger dataset than those currently available.