自生成兵棋AI:基于大型语言模型的双层Agent任务规划
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南京大学

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TP1

基金项目:

国家自然科学青年基金项目-基于人机融合的深度强化学习智能博弈决策机理研究(62306135)


Self generated Wargame AI: Double layer Agent Task Planning Based on Large Language Model
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Affiliation:

Nanjing University

Fund Project:

National Natural Science Youth Fund Project - Research on Intelligent Game Decision Mechanism of Deep Reinforcement Learning Based on Human Machine Integration (62306135)

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    摘要:

    ChatGPT所代表的大型语言模型对AI领域产生了颠覆性影响。但它主要关注自然语言处理、语音识别、机器学习和自然语言理解。这篇论文创新地将大型语言模型应用于智能决策领域,将大型语言模型置于决策中心,并构建以大型语言模型为核心的Agent体系结构。基于此,进一步提出了双层Agent任务规划,通过自然语言的交互,发出和执行决策指令,并通过兵棋推演模拟环境进行仿真验证。通过兵棋对抗模拟实验,发现大型语言模型的智能决策能力明显优于常用的强化学习AI,智能性、可理解性都更强。通过实验证明,大型语言模型的智能与Prompt密切相关。这项工作还将大型语言模型从以往的人机交互领域拓展到智能决策领域,对智能决策的发展具有重要的参考价值和意义。

    Abstract:

    The big language model represented by ChatGPT has had a disruptive impact on the field of artificial intelligence. But it mainly focuses on natural language processing, speech recognition, machine learning and natural-language understanding. This paper innovatively applies the big language model to the field of intelligent decision-making, places the big language model in the decision-making center, and constructs an agent architecture with the big language model as the core. Based on this, it further proposes a two-layer agent task planning, issues and executes decision commands through the interaction of natural language, and carries out simulation verification through the wargame simulation environment. Through the game confrontation simulation experiment, it is found that the intelligent decision-making ability of the big language model is significantly stronger than the commonly used reinforcement learning AI, and the intelligence, understandability and generalization are all better. And through experiments, it was found that the intelligence of the large language model is closely related to Prompt. This work also extends the large language model from previous human-computer interaction to the field of intelligent decision-making, which has important reference value and significance for the development of intelligent decision-making.

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  • 收稿日期:2023-10-26
  • 最后修改日期:2024-06-29
  • 录用日期:2024-03-11
  • 在线发布日期: 2024-04-10
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