The past decade has seen a rapid increase in the level of research interest in procedural content generation (PCG) for digital games, and there are now numerous research avenues focused on new approaches for driving and applying PCG systems. An area in which progress has been comparatively slow is the development of generalisable approaches for comparing alternative PCG systems, especially in terms of their generative spaces. It is to this area that this paper aims to make a contribution, by exploring the utility of data compression algorithms in compressing the generative spaces of PCG systems. We hope that this approach could be the basis for developing useful qualitative tools for comparing PCG systems to help designers better understand and optimize their generators. In this work we assess the efficacy of a selection of algorithms across sets of levels for 2D tile-based games by investigating how much their respective generative space compressions correlate with level behavioral characteristics. We conclude that the approach looks to be a promising one despite some inconsistency in efficacy in alternative domains, and that of the algorithms tested Multiple Correspondence Analysis appears to perform the most effectively.
翻译:过去十年来,对数字游戏的程序内容生成(PCG)的研究兴趣迅速提高,现在有许多研究渠道,侧重于驱动和应用PCG系统的新办法。进展相对缓慢的一个领域是开发通用方法,以比较替代性PCG系统,特别是其基因空间。正是在这一领域,本文件的目的是探索数据压缩算法在压缩PCG系统发源空间方面的效用,从而作出贡献。我们希望这种方法可以成为开发有用的定性工具的基础,以比较PCG系统,帮助设计者更好地了解和优化其生成器。在这项工作中,我们通过调查2D牌游戏各不同层次的算法与水平行为特征相关联的程度,评估了它们各自基因空间压缩法的功效。我们的结论是,尽管在替代领域的效率存在某些不一致之处,但这一方法看起来很有希望,而且经过测试的多种对应算法分析似乎能最有效地发挥作用。