GameGPT: Multi-agent Collaborative Framework for Game Development

• URL: http://arxiv.org/abs/2310.08067v1
• Date: Thu, 12 Oct 2023 06:31:43 GMT
• Title: GameGPT: Multi-agent Collaborative Framework for Game Development
• Authors: Dake Chen, Hanbin Wang, Yunhao Huo, Yuzhao Li, Haoyang Zhang
• Abstract summary: Large language model (LLM) based agents have demonstrated their capacity to automate and expedite software development processes. We propose a multi-agent collaborative framework, dubbed GameGPT, to automate game development.
• Score: 3.5100485879548127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The large language model (LLM) based agents have demonstrated their capacity to automate and expedite software development processes. In this paper, we focus on game development and propose a multi-agent collaborative framework, dubbed GameGPT, to automate game development. While many studies have pinpointed hallucination as a primary roadblock for deploying LLMs in production, we identify another concern: redundancy. Our framework presents a series of methods to mitigate both concerns. These methods include dual collaboration and layered approaches with several in-house lexicons, to mitigate the hallucination and redundancy in the planning, task identification, and implementation phases. Furthermore, a decoupling approach is also introduced to achieve code generation with better precision.

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