Strategic reasoning enables agents to cooperate, communicate, and compete with other agents in diverse situations. Existing approaches to solving strategic games rely on extensive training, yielding strategies that do not generalize to new scenarios or games without retraining. Large Language Models (LLMs), with their ability to comprehend and generate complex, context-rich language, could prove powerful as tools for strategic gameplay. This paper introduces an approach that uses pretrained LLMs with few-shot chain-of-thought examples to enable strategic reasoning for AI agents. Our approach uses systematically generated demonstrations of reasoning about states, values, and beliefs to prompt the model. Using extensive variations of simple matrix games, we show that strategies that are derived based on systematically generated prompts generalize almost perfectly to new game structures, alternate objectives, and hidden information. Additionally, we demonstrate our approach can lead to human-like negotiation strategies in realistic scenarios without any extra training or fine-tuning. Our results highlight the ability of LLMs, guided by systematic reasoning demonstrations, to adapt and excel in diverse strategic scenarios.

翻译：策略推理使智能体能够在不同情境中与其他智能体进行协作、沟通与竞争。现有解决策略博弈的方法依赖于大量训练，生成的策略无法泛化至新场景或无需重新训练的博弈。大型语言模型（LLMs）凭借其理解与生成复杂且富含上下文语言的能力，有望成为策略博弈的强大工具。本文提出一种利用预训练LLM结合少样本思维链示例实现AI智能体策略推理的方法。我们的方法采用系统生成的关于状态、价值和信念的推理示例来引导模型。通过对简单矩阵博弈的广泛变体进行实验，我们证明基于系统生成提示推导出的策略几乎能完美泛化至新的博弈结构、替代目标及隐藏信息。此外，我们展示了该方法可在无需额外训练或微调的情况下，在现实场景中产生类人谈判策略。我们的结果凸显了在系统性推理示例引导下，LLM能够适应并擅长多样化策略场景的能力。