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

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arxiv 2407.03460 v1 pith:L7CZV5AJ submitted 2024-07-03 cs.CL cs.AI

Sudha Rao , Weijia Xu , Michael Xu , Jorge Leandro , Ken Lobb , Gabriel DesGarennes , Chris Brockett , Bill Dolan This is my paper · ORCID
classification cs.CLcs.AI
keywords npcscollaborativegamellm-drivenminecraftmodelsplayerscharacters
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LLMs as Agentic Cooperative Players in Multiplayer UNO

    cs.AI 2025-09 conditional novelty 6.0 of 10

    LLMs can beat random agents in UNO, but as cooperative partners only LLaMA3.3-70B with cloze prompting gave a significant teammate win-rate gain (35.00% to 35.96%).

  2. MARCO: Meta-Reflection with Cross-Referencing for Code Reasoning

    cs.CL 2025-05 conditional novelty 6.0 of 10

    MARCO combines cross-problem knowledge accumulation with cross-agent lesson sharing to improve LLM code reasoning at inference time.

  3. Talking-to-Build: How LLM-Assisted Interface Shapes Player Performance and Experience in Minecraft

    cs.HC 2025-07 conditional novelty 5.0 of 10

    A 30-participant Minecraft study found that an LLM chat interface improved self-reported game experience compared with typed commands, while objective task performance was not measured.

  4. Understanding Persuasive Interactions between Generative Social Agents and Humans: The Knowledge-based Persuasion Model (KPM)

    cs.HC 2026-02 conditional novelty 4.0 of 10

    A proposed model says a generative social agent's self-, user-, and context-knowledge drives its persuasive behavior, which in turn shapes users' attitudes and behavior.

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