# Illuminating the Space of Enemies Through MAP-Elites

arXiv: https://arxiv.org/abs/2202.09615 · HTML: https://mlsanity.com/paper/2202.09615/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Procedural Content Generation through Quality Diversity](https://mlsanity.com/paper/1907.04053/) (2019)
- [Talakat: Bullet Hell Generation through Constrained Map-Elites](https://mlsanity.com/paper/1806.04718/) (2018)

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

- [The Quest for Content: A Survey of Search-Based Procedural Content Generation for Video Games](https://mlsanity.com/paper/2311.04710/) (2023)

## Abstract (arXiv)

Action-Adventure games have several challenges to overcome, where the most common are enemies. The enemies' goal is to hinder the players' progression by taking life points, and the way they hinder this progress is distinct for different kinds of enemies. In this context, this paper introduces an extended version of an evolutionary approach for procedurally generating enemies that target the enemy's difficulty as the goal. Our approach advances the enemy generation research by incorporating a MAP-Elites population to generate diverse enemies without losing quality. The computational experiment showed the method converged most enemies in the MAP-Elites in less than a second for most cases. Besides, we experimented with players who played an Action-Adventure game prototype with enemies we generated. This experiment showed that the players enjoyed most levels they played, and we successfully created enemies perceived as easy, medium, or hard to face.