The term Procedural Content Generation (PCG) refers to the (semi-)automatic generation of game content by algorithmic means, and its methods are becoming increasingly popular in game-oriented research and industry. A special class of these methods, which is commonly known as search-based PCG, treats the given task as an optimisation problem. Such problems are predominantly tackled by evolutionary algorithms. We will demonstrate in this paper that obtaining more information about the defined optimisation problem can substantially improve our understanding of how to approach the generation of content. To do so, we present and discuss three efficient analysis tools, namely diagonal walks, the estimation of high-level properties, as well as problem similarity measures. We discuss the purpose of each of the considered methods in the context of PCG and provide guidelines for the interpretation of the results received. This way we aim to provide methods for the comparison of PCG approaches and eventually, increase the quality and practicality of generated content in industry.

翻译：程序化内容生成（Procedural Content Generation，PCG）是指通过算法手段（半）自动生成游戏内容的方法，这类方法在游戏研究和工业界正日益普及。其中一类特殊方法（通常称为基于搜索的PCG）将给定任务视为优化问题，此类问题主要通过进化算法求解。本文旨在证明：获取关于已定义优化问题的更多信息，能够显著提升我们对内容生成方法的理解。为此，我们提出并讨论三种高效分析工具：对角线行走、高层属性估计以及问题相似性度量。我们结合PCG背景论述每种方法的用途，并提供结果解读指南。通过这种方式，我们旨在为PCG方法比较提供工具，最终提升工业界生成内容的质量和实用性。