Expressive Range Analysis (ERA), an approach for visualising the output of Procedural Content Generation (PCG) systems, is widely used within PCG research to evaluate and compare generators, often to make comparative statements about their relative performance in terms of output diversity and search space exploration. Producing a standard ERA visualisation requires the selection of two metrics which can be calculated for all generated artefacts to be visualised. However, to our knowledge there are no methodologies or heuristics for justifying the selection of a specific metric pair over alternatives. Prior work has typically either made a selection based on established but unjustified norms, designer intuition, or has produced multiple visualisations across all possible pairs. This work aims to contribute to this area by identifying valuable characteristics of metric pairings, and by demonstrating that pairings that have these characteristics have an increased probability of producing an informative ERA projection of the underlying generator. We introduce and investigate three quantifiable selection criteria for assessing metric pairs, and demonstrate how these criteria can be operationalized to rank those available. Though this is an early exploration of the concept of quantifying the utility of ERA metric pairs, we argue that the approach explored in this paper can make ERA more useful and usable for both researchers and game designers.

翻译：表现力范围分析（ERA）是一种可视化程序化内容生成（PCG）系统输出的方法，在PCG研究中被广泛用于评估和比较生成器，通常旨在就它们在输出多样性和搜索空间探索方面的相对性能进行比较性陈述。生成标准的ERA可视化需要选择两个可对所有待可视化生成工件进行计算的度量。然而，据我们所知，目前尚无用于证明特定度量对选择优于其他方案的方法或启发式规则。先前的研究通常要么依据既定但未经验证的规范或设计者直觉进行选择，要么对所有可能的度量对生成多个可视化结果。本研究旨在通过识别度量配对的有价值特性，并证明具有这些特性的配对更有可能生成底层生成器信息丰富的ERA投影，从而为这一领域做出贡献。我们引入并研究了三种可量化的度量对评估选择标准，并演示了如何将这些标准操作化以对可用度量进行排序。尽管这是对量化ERA度量对效用的早期探索，但我们认为本文探讨的方法可以使ERA对研究人员和游戏设计师更有用且更易用。