# Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites

arXiv: https://arxiv.org/abs/1906.05175 · HTML: https://mlsanity.com/paper/1906.05175/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 18 of 18)

- [Illuminating Diverse Neural Cellular Automata for Level Generation](https://mlsanity.com/paper/2109.05489/) (2021)
- [Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder](https://mlsanity.com/paper/2102.12463/) (2021)
- [QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration](https://mlsanity.com/paper/2308.03665/) (2023)
- [The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformers](https://mlsanity.com/paper/2303.16207/) (2023)
- [TropeTwist: Trope-based Narrative Structure Generation](https://mlsanity.com/paper/2204.09672/) (2022)
- [Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms](https://mlsanity.com/paper/2204.09828/) (2022)
- [Story Designer: Towards a Mixed-Initiative Tool to Create Narrative Structures](https://mlsanity.com/paper/2210.09294/) (2022)
- [Lode Encoder: AI-constrained co-creativity](https://mlsanity.com/paper/2308.01312/) (2023)
- [Diversity from Human Feedback](https://mlsanity.com/paper/2310.06648/) (2023)
- [Measuring Diversity of Game Scenarios](https://mlsanity.com/paper/2404.15192/) (2024)
- [Generative Design through Quality-Diversity Data Synthesis and Language Models](https://mlsanity.com/paper/2405.09997/) (2024)
- [Safety Must Precede the Deployment of Open-Ended AI](https://mlsanity.com/paper/2502.04512/) (2025)
- [Exploring open-ended gameplay features with Micro RollerCoaster Tycoon](https://mlsanity.com/paper/2105.04342/) (2021)
- [Illuminating the Space of Dungeon Maps, Locked-door Missions and Enemy Placement Through MAP-Elites](https://mlsanity.com/paper/2202.09301/) (2022)
- [Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials](https://mlsanity.com/paper/2204.05217/) (2022)
- [From Metrics to Meaning: Time to Rethink Evaluation in Human-AI Collaborative Design](https://mlsanity.com/paper/2402.07911/) (2024)
- [Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization](https://mlsanity.com/paper/2408.16393/) (2024)
- [Procedural Generation of First Person Shooter Maps using Map-Elites](https://mlsanity.com/paper/2605.30570/) (2026)

## Abstract (arXiv)

We propose the use of quality-diversity algorithms for mixed-initiative game content generation. This idea is implemented as a new feature of the Evolutionary Dungeon Designer, a system for mixed-initiative design of the type of levels you typically find in computer role playing games. The feature uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions. Users can flexibly and dynamically choose relevant dimensions of variation, and incorporate suggestions produced by the algorithm in their map designs. At the same time, any modifications performed by the human feed back into MAP-Elites, and are used to generate further suggestions.