Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials
This addresses the problem of creating tailored tutorial content for game developers, though it appears incremental as it builds on existing procedural generation and quality-diversity methods.
The paper tackles automated generation of video game tutorial levels by using procedural personas to guide a quality-diversity algorithm, resulting in maps that strongly encourage or discourage specific playstyle behaviors and range from simple to complex designs.
In this paper, we present a method for automated persona-driven video game tutorial level generation. Tutorial levels are scenarios in which the player can explore and discover different rules and game mechanics. Procedural personas can guide generators to create content which encourages or discourages certain playstyle behaviors. In this system, we use procedural personas to calculate the behavioral characteristics of levels which are evolved using the quality-diversity algorithm known as Constrained MAP-Elites. An evolved map's quality is determined by its simplicity: the simpler it is, the better it is. Within this work, we show that the generated maps can strongly encourage or discourage different persona-like behaviors and range from simple solutions to complex puzzle-levels, making them perfect candidates for a tutorial generative system.