  We explore how interaction with large language models (LLMs) can give rise to
emergent behaviors, empowering players to participate in the evolution of game
narratives. Our testbed is a text-adventure game in which players attempt to
solve a mystery under a fixed narrative premise, but can freely interact with
non-player characters generated by GPT-4, a large language model. We recruit 28
gamers to play the game and use GPT-4 to automatically convert the game logs
into a node-graph representing the narrative in the player's gameplay. We find
that through their interactions with the non-deterministic behavior of the LLM,
players are able to discover interesting new emergent nodes that were not a
part of the original narrative but have potential for being fun and engaging.
Players that created the most emergent nodes tended to be those that often
enjoy games that facilitate discovery, exploration and experimentation.
