Adapting Procedural Content Generation to Player Personas Through Evolution
This work addresses game development by enabling personalized content generation, but it is incremental as it builds on existing procedural content generation and persona-based methods.
The paper tackled the problem of automatically adapting procedurally generated game levels to specific player personas, and demonstrated that their architecture successfully tailored levels for four rule-based persona agents across three experience metrics, showing persona-conscious adaptation rather than general optimizations.
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.