GameGPT: Multi-agent Collaborative Framework for Game Development

2023-10-12 · Dake Chen, Hanbin Wang, Yunhao Huo, Yuzhao Li, Haoyang Zhang

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.

📄 PDF Abstract BibTeX arXiv:2310.08067

Code (1)

geekan/metagpt

Tasks

Code GenerationHallucinationLanguage ModelingLanguage ModellingLarge Language Model

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