Space segmentation and multiple autonomous agents: a Minecraft settlement generator
Sebastian S. Christiansen, Marco Scirea · 2022
This paper describes and illustrates a system designed as part of a submission to the Generative Design in Minecraft (GDMC) competition. It introduces an approach to partitioning of a three-dimensional game space novel to the domain of Minecraft settlement generation: traversal segmentation. Moreover, the paper introduces a novel implementation of a two-system brain model for autonomous agent simulation. Traversal segmentation is used in conjunction with a grid-wise segmentation method to produce a contextual representation of the game space. This is used as input for the settlement generation using autonomous agents, where each agent is controlled by their system 1 impulses, and their system 2 reasoning-action brain model. The two-system brain model is novel to autonomous agent simulation and is described both theoretically and by its implementation. The resulting settlements, generated by settlers upon grid-wise segmentation of the traversable space, boasts the properties; organic settlement evolution, and adaptability to its surrounding terrain, though not realism when compared to settlements procedurally generated by Minecraft.