Illuminating the Space of Enemies Through MAP-Elites
This addresses the challenge of enemy design for game developers, but it is incremental as it builds on existing evolutionary methods.
The paper tackles the problem of procedurally generating diverse enemies in action-adventure games by introducing an extended evolutionary approach that uses MAP-Elites to target enemy difficulty, resulting in convergence in less than a second for most cases and successfully creating enemies perceived as easy, medium, or hard by players.
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.