5 research outputs found
Generating Interactive Worlds with Text
Procedurally generating cohesive and interesting game environments is
challenging and time-consuming. In order for the relationships between the game
elements to be natural, common-sense has to be encoded into arrangement of the
elements. In this work, we investigate a machine learning approach for world
creation using content from the multi-player text adventure game environment
LIGHT. We introduce neural network based models to compositionally arrange
locations, characters, and objects into a coherent whole. In addition to
creating worlds based on existing elements, our models can generate new game
content. Humans can also leverage our models to interactively aid in
worldbuilding. We show that the game environments created with our approach are
cohesive, diverse, and preferred by human evaluators compared to other machine
learning based world construction algorithms