Adapting Procedural Content Generation to Player Personas Through Evolution

Abstract: Automatically adapting game content to players opens new doors for game development. In this paper we propose an architecture using persona agents and experience metrics, which enables evolving procedurally generated levels tailored for particular player personas. Using our game, "Grave Rave", we demonstrate that this approach successfully adapts to four rule-based persona agents over three different experience metrics. Furthermore, the adaptation is shown to be specific in nature, meaning that the levels are persona-conscious, and not just general optimizations with regard to the selected metric.