# Level Generation with Constrained Expressive Range

arXiv: https://arxiv.org/abs/2504.05334 · HTML: https://mlsanity.com/paper/2504.05334/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Super Mario as a String: Platformer Level Generation Via LSTMs](https://mlsanity.com/paper/1603.00930/) (2016)
- [Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder](https://mlsanity.com/paper/2102.12463/) (2021)
- [Controllable Level Blending between Games using Variational Autoencoders](https://mlsanity.com/paper/2002.11869/) (2020)
- [Sequential Segment-based Level Generation and Blending using Variational Autoencoders](https://mlsanity.com/paper/2007.08746/) (2020)
- [Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature Dimensions](https://mlsanity.com/paper/2003.03377/) (2020)
- [The Right Variety: Improving Expressive Range Analysis with Metric Selection Methods](https://mlsanity.com/paper/2304.02366/) (2023)
- [Exploring Minecraft Settlement Generators with Generative Shift Analysis](https://mlsanity.com/paper/2309.05371/) (2023)
- [Tabletop Roleplaying Games as Procedural Content Generators](https://mlsanity.com/paper/2007.06108/) (2020)

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

- [Level Generation with Quantum Reservoir Computing](https://mlsanity.com/paper/2505.13287/) (2025)

## Abstract (arXiv)

Expressive range analysis is a visualization-based technique used to evaluate the performance of generative models, particularly in game level generation. It typically employs two quantifiable metrics to position generated artifacts on a 2D plot, offering insight into how content is distributed within a defined metric space. In this work, we use the expressive range of a generator as the conceptual space of possible creations. Inspired by the quality diversity paradigm, we explore this space to generate levels. To do so, we use a constraint-based generator that systematically traverses and generates levels in this space. To train the constraint-based generator we use different tile patterns to learn from the initial example levels. We analyze how different patterns influence the exploration of the expressive range. Specifically, we compare the exploration process based on time, the number of successful and failed sample generations, and the overall interestingness of the generated levels. Unlike typical quality diversity approaches that rely on random generation and hope to get good coverage of the expressive range, this approach systematically traverses the grid ensuring more coverage. This helps create unique and interesting game levels while also improving our understanding of the generator's strengths and limitations.