As the field of AI continues to evolve, a significant dimension of this progression is the development of Large Language Models and their potential to enhance multi-agent artificial intelligence systems. This paper explores the cooperative capabilities of Large Language Model-augmented Autonomous Agents (LAAs) using the well-known Meltin Pot environments along with reference models such as GPT4 and GPT3.5. Preliminary results suggest that while these agents demonstrate a propensity for cooperation, they still struggle with effective collaboration in given environments, emphasizing the need for more robust architectures. The study's contributions include an abstraction layer to adapt Melting Pot game scenarios for LLMs, the implementation of a reusable architecture for LLM-mediated agent development - which includes short and long-term memories and different cognitive modules, and the evaluation of cooperation capabilities using a set of metrics tied to the Melting Pot's "Commons Harvest" game. The paper closes, by discussing the limitations of the current architectural framework and the potential of a new set of modules that fosters better cooperation among LAAs.

翻译：随着人工智能领域的持续发展，大语言模型及其增强多智能体人工智能系统的潜力成为这一进步的重要维度。本文利用知名的Melting Pot环境以及GPT4、GPT3.5等参考模型，探索了大语言模型增强自主智能体（LAAs）的合作能力。初步结果表明，尽管这些智能体展现出合作倾向，但在特定环境中仍难以实现有效协作，凸显了构建更鲁棒架构的必要性。本研究的主要贡献包括：设计用于适配Melting Pot游戏场景的LLM抽象层；实现可复用的LLM中介智能体开发架构（包含短期与长期记忆及不同认知模块）；以及基于Melting Pot"公共资源收获"游戏的多项指标评估合作能力。本文最后讨论了当前架构框架的局限性，并提出了促进LAAs间更优合作的新模块集的可能性。