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World-building, the process of developing both the narrative and physicalworld of a game, plays a vital role in the game's experience. Criticallyacclaimed independent and AAA video games are praised for strong worldbuilding, with game maps that masterfully intertwine with and elevate thenarrative, captivating players and leaving a lasting impression. However,designing game maps that support a desired narrative is challenging, as itrequires satisfying complex constraints from various considerations. Mostexisting map generation methods focus on considerations about gameplaymechanics or map topography, while the need to support the story is typicallyneglected. As a result, extensive manual adjustment is still required to designa game world that facilitates particular stories. In this work, we approachthis problem by introducing an extra layer of plot facility layout design thatis independent of the underlying map generation method in a world-buildingpipeline. Concretely, we present a system that leverages Reinforcement Learning(RL) to automatically assign concrete locations on a game map to abstractlocations mentioned in a given story (plot facilities), following spatialconstraints derived from the story. A decision-making agent moves the plotfacilities around, considering their relationship to the map and each other, tolocations on the map that best satisfy the constraints of the story. Our systemconsiders input from multiple modalities: map images as pixels, facilitylocations as real values, and story constraints expressed in natural language.We develop a method of generating datasets of facility layout tasks, create anRL environment to train and evaluate RL models, and further analyze thebehaviors of the agents through a group of comprehensive experiments andablation studies, aiming to provide insights for RL-based plot facility layoutdesign.

PlotMap: Automated Layout Design for Building Game Worlds