论文标题
压缩和比较程序内容生成器的生成空间
Compressing and Comparing the Generative Spaces of Procedural Content Generators
论文作者
论文摘要
在过去的十年中，数字游戏的过程内容生成（PCG）的研究兴趣水平迅速提高，现在有许多研究途径着重于用于驾驶和应用PCG系统的新方法。进步相对较慢的领域是开发可用的方法来比较替代PCG系统，尤其是在其生成空间方面。本文旨在通过探索数据压缩算法在压缩PCG系统的生成空间中的实用性来做出贡献。我们希望这种方法可以成为开发有用的定性工具来比较PCG系统以帮助设计师更好地理解和优化其发电机的基础。在这项工作中，我们通过研究其各自的生成空间压缩与级别的行为特征相关的程度，评估了基于2D瓦的游戏的一组级别算法的功效。我们得出的结论是，尽管替代域的功效有一些不一致，但这种方法看起来是一种有希望的方法，而测试的多个对应分析的算法似乎最有效地执行了这种算法。
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.
