World-building, the process of developing both the narrative and physical world of a game, plays a vital role in the game's experience. Critically-acclaimed independent and AAA video games are praised for strong world-building, with game maps that masterfully intertwine with and elevate the narrative, captivating players and leaving a lasting impression. However, designing game maps that support a desired narrative is challenging, as it requires satisfying complex constraints from various considerations. Most existing map generation methods focus on considerations about gameplay mechanics or map topography, while the need to support the story is typically neglected. As a result, extensive manual adjustment is still required to design a game world that facilitates particular stories. In this work, we approach this problem by introducing an extra layer of plot facility layout design that is independent of the underlying map generation method in a world-building pipeline. Concretely, we define (plot) facility layout tasks as the tasks of assigning concrete locations on a game map to abstract locations mentioned in a given story (plot facilities), following spatial constraints derived from the story. We present two methods for solving these tasks automatically: an evolutionary computation based approach through Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and a Reinforcement Learning (RL) based approach. We develop a method of generating datasets of facility layout tasks, create a gym-like environment for experimenting with and evaluating different methods, and further analyze the two methods with comprehensive experiments, aiming to provide insights for solving facility layout tasks. We will release the code and a dataset containing 10, 000 tasks of different scales.

翻译：世界构建——即开发游戏的叙事与物理世界的过程——在游戏体验中扮演着至关重要的角色。广受好评的独立游戏与AAA级电子游戏均因其出色的世界构建而备受赞誉，这些游戏的地图巧妙地将叙事交织其中并加以升华，从而吸引玩家并留下持久印象。然而，设计能够支撑预期叙事的游戏地图极具挑战性，因为这需要满足来自多方面考量的复杂约束。现有的地图生成方法大多侧重于游戏机制或地形地貌的考量，而支撑故事的需求通常被忽视。因此，要设计出能够促进特定故事发展的游戏世界，仍需大量的人工调整。在本研究中，我们通过在世界构建流程中引入一个独立于底层地图生成方法的额外“剧情设施布局设计”层来解决这一问题。具体而言，我们将（剧情）设施布局任务定义为：根据故事衍生的空间约束，将给定故事（剧情设施）中提及的抽象位置分配到游戏地图具体位置的任务。我们提出了两种自动解决这些任务的方法：一种基于协方差矩阵自适应进化策略（CMA-ES）的进化计算方法，以及一种基于强化学习（RL）的方法。我们开发了生成设施布局任务数据集的方法，创建了一个用于实验和评估不同方法的类gym环境，并通过综合实验进一步分析了这两种方法，旨在为设施布局任务的解决提供洞见。我们将公开代码及包含10,000个不同规模任务的数据集。