DreamGarden: A Designer Assistant for Growing Games from a Single Prompt

要旨

Coding assistants are increasingly leveraged in game design, both generating code and making high-level plans. To what degree can these tools align with developer workflows, and what new modes of human-computer interaction can emerge from their use? We present DreamGarden, an AI system capable of assisting with the development of diverse game environments in Unreal Engine. At the core of our method is an LLM-driven planner, capable of breaking down a single, high-level prompt---a dream, memory, or imagined scenario provided by a human user---into a hierarchical action plan, which is then distributed across specialized submodules facilitating concrete implementation. This system is presented to the user as a garden of plans and actions, both growing independently and responding to user intervention via seed prompts, pruning, and feedback. Through a user study, we explore design implications of this system, charting courses for future work in semi-autonomous assistants and open-ended simulation design.

受賞
Best Paper
著者
Sam Earle
New York University, Brooklyn, New York, United States
Samyak Parajuli
The University of Texas at Austin, Austin, Texas, United States
Andrzej Banburski-Fahey
Microsoft, Redmond, Washington, United States
DOI

10.1145/3706598.3714233

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714233

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Agent Design

G301
7 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
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