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Computer Science > Computation and Language

arXiv:2404.17027 (cs)
[Submitted on 25 Apr 2024]

Title:Player-Driven Emergence in LLM-Driven Game Narrative

Authors:Xiangyu Peng, Jessica Quaye, Weijia Xu, Chris Brockett, Bill
Dolan, Nebojsa Jojic, Gabriel DesGarennes, Ken Lobb, Michael Xu,
Jorge Leandro, Claire Jin, Sudha Rao
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Game Narrative, by Xiangyu Peng and 11 other authors
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    Abstract: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.

Subjects:          Computation and Language (cs.CL); Artificial
                   Intelligence (cs.AI)
Cite as:           arXiv:2404.17027 [cs.CL]
                   (or arXiv:2404.17027v1 [cs.CL] for this version)
                   https://doi.org/10.48550/arXiv.2404.17027
                   Focus to learn more
                   arXiv-issued DOI via DataCite
Journal reference: IEEE Conference on Games 2024

Submission history

From: Sudha Rao [view email]
[v1] Thu, 25 Apr 2024 20:39:44 UTC (1,287 KB)
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