Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft
This incremental work addresses the problem of integrating LLM-driven NPCs for collaborative gameplay, informing future game developers.
The study investigated how human players collaborate with GPT-4-driven non-player characters in a Minecraft minigame to complete quests, finding emergent patterns of collaborative behavior based on feedback from 28 players.
The use of generative AI in video game development is on the rise, and as the conversational and other capabilities of large language models continue to improve, we expect LLM-driven non-player characters (NPCs) to become widely deployed. In this paper, we seek to understand how human players collaborate with LLM-driven NPCs to accomplish in-game goals. We design a minigame within Minecraft where a player works with two GPT4-driven NPCs to complete a quest. We perform a user study in which 28 Minecraft players play this minigame and share their feedback. On analyzing the game logs and recordings, we find that several patterns of collaborative behavior emerge from the NPCs and the human players. We also report on the current limitations of language-only models that do not have rich game-state or visual understanding. We believe that this preliminary study and analysis will inform future game developers on how to better exploit these rapidly improving generative AI models for collaborative roles in games.