GameGPT: Multi-agent Collaborative Framework for Game Development
This work addresses automation challenges in game development for developers, though it appears incremental as it builds on existing LLM-based agent frameworks.
The authors tackled the problem of automating game development with LLM-based agents by proposing GameGPT, a multi-agent collaborative framework that addresses hallucination and redundancy concerns through dual collaboration, layered approaches, and decoupling methods, achieving improved code generation precision.
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.