PlotMap: Automated Layout Design for Building Game Worlds

TL;DR

PlotMap tackles the challenge of designing game maps that support a given narrative by introducing plot facilities as an abstract layer that sits atop any map generation method, formalized as the task $ \langle \mathcal{F}, \mathcal{T}, \mathcal{C} \rangle $. It develops two automatic solvers: an evolutionary CMA-ES method for polygon-based maps and a reinforcement learning method for pixel-based maps, and provides a 10,000-task dataset with 12 constraint types across 9 biomes within a Gym-like environment for evaluation. The work demonstrates that CMA-ES delivers rapid, accurate layouts at practical scales, while the RL approach enables smoother designer interaction and adaptability to iterative edits, with dataset and code release to support reproducibility. Together, these methods offer a versatile, narrative-aware design framework that can plug into existing story and map generation pipelines and enable broader applications in game design and beyond. $

Abstract

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.

Figures (7)

• Figure 1: Accommodating a story on a map with a plot facility layout design process
• Figure 2: We derive spatial constraints from a story and layout locations mentioned in the story on a map to satisfy the constraints.
• Figure 3: Constraint types included in the $10,000$-task dataset (each $p_i$ represents a plot facility). A constraint type $ConstraintType(b_1, \dots, b_m, p_1, \dots, p_n)$ is instantiated to become a constraint by substituting each of $b_1, \dots, b_m$ with a biome type, and each of $p_1, \dots, p_m$ with a plot facility id ($m \geq 0, n\geq 0$).
• Figure 4: Layout evaluations to reach a level of constraint satisfaction with 10, 30 and 60 facilities, and 30, 60, and 90 constraints to satisfy. Median satisfaction over 1000 different tuples of terrain and constraints, shaded regions indicate 1st and 3rd quartiles. Adaptive population sizes and cause generation times to vary from 0.25 to 0.75 seconds. However, performing a run with a budget of 20k evaluations consistently takes 2-5 minutes.
• Figure 5: Cooperative behavior to satisfy constraints: Marketown and Veilstead Kingdom must be across a lake from each other, while Aquafrost Garrison must be to the south of Marketown. Aquafrost Garrison was initially to the south of Marketown but as Marketown moves south to be across the lake from Veilstead Kingdom, Aquafrost Garrison moves even further south to continue satisfying its south of Marketown constraint.