Self Generated Wargame AI: Double Layer Agent Task Planning Based on Large Language Model (2312.01090v2)

Abstract: The LLMs represented by ChatGPT have 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 LLM to the field of intelligent decision-making, places the LLM in the decision-making center, and constructs an agent architecture with the LLM 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 LLM is significantly stronger than the commonly used reinforcement learning AI and rule AI, and the intelligence, understandability and generalization are all better. And through experiments, it was found that the intelligence of the LLM is closely related to prompt. This work also extends the LLM 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.