PlotMap: Automated Layout Design for Building Game Worlds

• URL: http://arxiv.org/abs/2309.15242v4
• Date: Fri, 16 Aug 2024 04:16:12 GMT
• Title: PlotMap: Automated Layout Design for Building Game Worlds
• Authors: Yi Wang, Jieliang Luo, Adam Gaier, Evan Atherton, Hilmar Koch,
• Abstract summary: We introduce an extra layer of plot facility layout design that is independent of the underlying map generation method in a world-building pipeline. We present two methods for solving these tasks automatically: an evolutionary computation based approach throughCMA-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.
• Score: 4.74497343690049
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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