GRIM: GRaph-based Interactive narrative visualization for gaMes
Jorge Leandro Sudha Rao Michael Xu Weijia Xu Nebosja Jojic Chris Brockett Bill Dolan
Dialogue-based Role Playing Games (RPGs) require powerful storytelling. The
narratives of these may take years to write and typically involve a large
creative team. In this work, we demonstrate the potential of large generative
text models to assist this process. \textbf{GRIM}, a prototype
\textbf{GR}aph-based \textbf{I}nteractive narrative visualization system for
ga\textbf{M}es, generates a rich narrative graph with branching storylines that
match a high-level narrative description and constraints provided by the
designer. Game designers can interactively edit the graph by automatically
generating new sub-graphs that fit the edits within the original narrative and
constraints. We illustrate the use of \textbf{GRIM} in conjunction with GPT-4,
generating branching narratives for four well-known stories with different
contextual constraints.