The researchers explored how player interactions with large language models (LLMs) can lead to the emergence of novel and creative narrative paths in a text-adventure game called "Dejaboom!". In this game, players attempt to solve a mystery by freely conversing with non-player characters (NPCs) generated in real-time by the GPT-4 language model.
The researchers recruited 28 players to engage with the game and analyzed the narrative graphs constructed from their gameplay logs. They found that through their interactions with the non-deterministic behavior of the LLM, players were able to discover interesting new "emergent nodes" in the narrative graph that were not part of the original game design, but had the potential to be engaging and fun.
The emergent nodes fell into several categories, including:
The researchers also found that the players who created the most emergent nodes tended to be those who enjoy games that facilitate discovery, exploration, and experimentation. This suggests that players with creative motivation profiles may be well-suited to contribute to a more collaborative model of game development, where designers, players, and LLMs work together to shape the narrative experience.
The study highlights the potential for LLMs to empower players and introduce emergent behaviors in game narratives, while also identifying areas for improvement, such as reducing latency and ensuring consistent NPC personas.
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