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arXiv 2207.02100cs.AI
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生成不同行为参与的游戏级别

Generating Game Levels of Diverse Behaviour Engagement

Southern University of Science and Technology (SUSTech)(南方科技大学)

Keyuan Zhang, Jiayu Bai, Jialin Liu

摘要:

近年来,人们对经验驱动的过程级生成越来越感兴趣。已经制定了各种指标来模拟玩家体验,并帮助生成个性化的水平。在这项工作中,我们的问题是否经验指标可以适应代理不同的人物角色。我们首先回顾评估游戏水平的现有指标。然后,专注于平台游戏,我们设计了一个框架,集成各种代理和评估指标。对\emph{Super Mario Bros.}的实验研究表明,使用相同的评价指标,但不同的人物角色的代理可以产生特定的人物角色的水平。这意味着,对于简单的游戏,使用特定玩家原型的游戏代理作为水平测试器可能是我们生成不同行为参与水平所需的全部。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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