Related papers: Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft

Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft

URL: http://arxiv.org/abs/2407.03460v1
Date: Wed, 3 Jul 2024 19:11:21 GMT
Title: Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft
Authors: Sudha Rao, Weijia Xu, Michael Xu, Jorge Leandro, Ken Lobb, Gabriel DesGarennes, Chris Brockett, Bill Dolan,
Abstract summary: We design a minigame within Minecraft where a player works with two GPT4-driven NPCs to complete a quest. On analyzing the game logs and recordings, we find that several patterns of collaborative behavior emerge from the NPCs and the human players. 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.
Score: 14.877848057734463
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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