# Illuminating the Space of Dungeon Maps, Locked-door Missions and Enemy Placement Through MAP-Elites

arXiv: https://arxiv.org/abs/2202.09301 · HTML: https://mlsanity.com/paper/2202.09301/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 18 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents](https://mlsanity.com/paper/1712.06560/) (2017)
- [Procedural Content Generation through Quality Diversity](https://mlsanity.com/paper/1907.04053/) (2019)
- [Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites](https://mlsanity.com/paper/1906.05175/) (2019)
- [Mech-Elites: Illuminating the Mechanic Space of GVGAI](https://mlsanity.com/paper/2002.04733/) (2020)

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

- [Procedural Generation of First Person Shooter Maps using Map-Elites](https://mlsanity.com/paper/2605.30570/) (2026)

## Abstract (arXiv)

Procedural Content Generation (PCG) methods are valuable tools to speed up the game development process. Moreover, PCG may also present in games as features, such as the procedural dungeon generation (PDG) in Moonlighter (Digital Sun, 2018). This paper introduces an extended version of an evolutionary dungeon generator by incorporating a MAP-Elites population. Our dungeon levels are discretized with rooms that may have locked-door missions and enemies within them. We encoded the dungeons through a tree structure to ensure the feasibility of missions. We performed computational and user feedback experiments to evaluate our PDG approach. They show that our approach accurately converges almost the whole MAP-Elite population for most executions. Finally, players' feedback indicates that they enjoyed the generated levels, and they could not indicate an algorithm as a level generator.