  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.
