Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials

TL;DR

This paper introduces a method for generating tutorial levels in video games tailored to different player personas, using evolutionary algorithms to create levels that promote or discourage specific playstyles.

Contribution

It presents a novel approach combining procedural personas with quality-diversity algorithms to generate diverse tutorial levels that influence player behavior.

Findings

Generated maps effectively encourage or discourage specific behaviors.

Levels range from simple to complex, suitable for tutorials.

The method demonstrates strong control over player experience.

Abstract

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.