Player-Driven Emergence in LLM-Driven Game Narrative
<|reference_start|>Player-Driven Emergence in LLM-Driven Game Narrative: 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.<|reference_end|>
@article{peng2024player-driven,
    title={Player-Driven Emergence in LLM-Driven Game Narrative},
    author={Xiangyu Peng, Jessica Quaye, Sudha Rao, Weijia Xu, Portia Botchway,
  Chris Brockett, Nebojsa Jojic, Gabriel DesGarennes, Ken Lobb, Michael Xu,
  Jorge Leandro, Claire Jin, Bill Dolan},
    journal={IEEE Conference on Games 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17027},
    primaryClass={cs.CL cs.AI}
}