Illuminating Game Space Using MAP-Elites for Assisting Video Game Design

In this paper we demonstrate the use of Multidimensional Archive of Phenotypic Elites (MAP-Elites), a divergent search algorithm, as a game design assisting tool. The MAP-Elites algorithm allows illumination in the game space instead of just determining a single game setting via objective based optimization. We showed how the game space can be explored by generating a diverse set of game settings, allowing the designers to explore what range of behaviours are possible in their games. The proposed method was applied to the 2D game Cave Swing. We discovered different settings of the game where a Rolling Horizon Evolutionary Algorithm (RHEA) agent behaved differently depending on the selected game parameters. The agent’s performance was plotted against its behaviour for further exploration, which allowed visualizing how the agent performed with selected behaviour traits.