Searching for good and diverse game levels
Searching for good and diverse game levels
复制标题
寻找优秀且多样化的游戏关卡
DOI：
10.1109/cig.2014.6932908
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
J. Togelius
中科院分区：
文献类型：
--
作者：
M. Preuss;Antonios Liapis;J. Togelius
在过程性内容生成中，人们通常对生成大量的人工产物感兴趣，这些人工产物不仅具有高质量，而且在游戏性、视觉印象或一些其他标准方面也是多样的。我们研究了几种基于搜索的方法来创建好的和多样化的游戏内容，特别是基于进化策略的方法，包括有或没有多样性保存机制、新颖性搜索和随机搜索。内容域是游戏关卡，更准确地说是战略游戏的地图示意图，这些示意图在Sentient Sketchbook设计工具中用作建议。对于这种类型的内容，有几个多样性度量是可能的：我们调查基于瓷砖的、基于客观的和视觉印象距离。我们发现，具有多样性保护机制的进化可以产生良好和多样化的内容，但只有在使用适当的距离度量时才能产生。相反，通过比较保持多样性的进化算法和盲重启进化算法，我们可以得出这些距离度量是否适合该领域的结论。
In procedural content generation, one is often interested in generating a large number of artifacts that are not only of high quality but also diverse, in terms of gameplay, visual impression or some other criterion. We investigate several search-based approaches to creating good and diverse game content, in particular approaches based on evolution strategies with or without diversity preservation mechanisms, novelty search and random search. The content domain is game levels, more precisely map sketches for strategy games, which are meant to be used as suggestions in the Sentient Sketchbook design tool. Several diversity metrics are possible for this type of content: we investigate tile-based, objective-based and visual impression distance. We find that evolution with diversity preservation mechanisms can produce both good and diverse content, but only when using appropriate distance measures. Reversely, we can draw conclusions about the suitability of these distance measures for the domain from the comparison of diversity preserving versus blind restart evolutionary algorithms.