Generating Game Levels of Diverse Behaviour Engagement

TL;DR

This paper explores how different agent personas can be used with existing experience metrics to generate game levels tailored to diverse player behaviors, demonstrating the approach with Super Mario Bros.

Contribution

It introduces a framework that integrates various agents and metrics for level generation, showing that persona-specific agents can produce levels engaging for different player types.

Findings

Using different agent personas yields levels suited to specific behaviors.

Experience metrics can be adapted for diverse agent archetypes.

Persona-specific agents effectively generate tailored game levels.

Abstract

Recent years, there has been growing interests in experience-driven procedural level generation. Various metrics have been formulated to model player experience and help generate personalised levels. In this work, we question whether experience metrics can adapt to agents with different personas. We start by reviewing existing metrics for evaluating game levels. Then, focusing on platformer games, we design a framework integrating various agents and evaluation metrics. Experimental studies on \emph{Super Mario Bros.} indicate that using the same evaluation metrics but agents with different personas can generate levels for particular persona. It implies that, for simple games, using a game-playing agent of specific player archetype as a level tester is probably all we need to generate levels of diverse behaviour engagement.