Illuminating the Space of Enemies Through MAP-Elites

TL;DR

This paper presents an enhanced evolutionary method using MAP-Elites to generate diverse, difficulty-targeted enemies for Action-Adventure games, achieving rapid convergence and positive player experience across difficulty levels.

Contribution

It introduces a novel MAP-Elites-based approach for procedurally generating diverse enemies with targeted difficulty, improving quality and diversity in enemy design.

Findings

Most enemies converged in less than a second in the MAP-Elites.

Players enjoyed most levels with generated enemies.

Successfully created enemies perceived as easy, medium, or hard.

Abstract

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.