Content generation is one of the major challenges in the modern age. The video game industry is no exception and the ever-increasing demand for bigger titles containing vast volumes of content has become one of the vital challenges for the content generation domain. Conventional game development as a human product is not cost efficient and the need for more intelligent, advanced and procedural methods is evident in this field. In a sense, procedural content generation (PCG) is a Non-deterministic Polynomial-Hard optimization problem in which specific metrics should be optimized. In this paper, we use the Estimation of Distribution Algorithm (EDA) to optimize the task of PCG in digital video games. EDA is an evolutionary stochastic optimization method and the introduction of probabilistic modeling as one of the main features of EDA into this problem domain is a reliable way to mathematically apply human knowledge to the challenging field of content generation. Acceptable performance of the proposed method is reflected in the results, which can inform the academia of PCG and contribute to the game industry.

内容生成是现代时代的主要挑战之一。视频游戏行业也不例外，对包含大量内容的大型游戏的不断增长的需求已成为内容生成领域的重要挑战之一。作为人类产品的传统游戏开发并不具有成本效益，并且在该领域显然需要更智能、更先进和程序化的方法。从某种意义上说，过程内容生成 (PCG) 是一个非确定性多项式硬优化问题，其中应优化特定指标。在本文中，我们使用分布估计算法 (EDA) 来优化 PCG 在数字视频游戏中的任务。EDA 是一种进化随机优化方法，将概率建模作为 EDA 的主要特征之一引入该问题领域是将人类知识在数学上应用于具有挑战性的内容生成领域的可靠方法。所提出方法的可接受性能反映在结果中，这可以为 PCG 学术界提供信息并为游戏行业做出贡献。