GameGPT: Multi-agent Collaborative Framework for Game Development

Autor: Chen, Dake, Wang, Hanbin, Huo, Yunhao, Li, Yuzhao, Zhang, Haoyang
Rok vydání: 2023
Předmět:
Druh dokumentu: Working Paper
Popis: 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.
Databáze: arXiv