Towards Computational Support with Language Models for TTRPG Game Masters
Jack Kelly, Michael Mateas, Noah Wardrip–Fruin
Abstract
Tabletop role-playing games require game masters to mediate between the desires of a group of players and the affordances of a mechanically rich game world. In this paper, we explore the potential for large language models like GPT-3 to assist with the improvisational needs of a game master. We build upon Shoelace, a computational assistant for the GUMSHOE One-2-One role-playing framework; using GPT-3, we extend Shoelace to provide dialogue suggestions for non-player characters as well as to highlight relevant game module information. We emphasize the potential for language models to facilitate new role-playing tools and mechanics and suggest that the user experience problems presented by language models and role-playing are still underexplored and unresolved.