Generating Game Levels of Diverse Behaviour Engagement
This work addresses the challenge of generating personalized game levels for different player archetypes, but it is incremental as it builds on existing metrics and focuses on simple games.
The paper tackled the problem of whether experience metrics can adapt to agents with different personas for procedural level generation in platformer games, and found that using the same metrics with agents of specific personas can generate levels tailored to those personas, as demonstrated in experiments on Super Mario Bros.
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.