基于大语言模型的开放式兵棋推演
Open-Ended Wargames with Large Language Models
AI总结：
本研究提出由大语言模型驱动的多智能体系统Snow Globe，可自动化开展定性开放式兵棋推演，支持全流程AI、人类或协作执行，已开源并通过两类推演案例验证，拓展了兵棋推演自动化的应用边界。
摘要：
兵棋推演是理解和演练现实决策的有力工具。利用人工智能（AI）自动开展兵棋推演，能够实现人类推演无法企及的可能性，例如多次重复推演以获取一系列可能的结果。兵棋推演分为两类：一类是定量推演，具有离散的行动类型；另一类是定性推演，围绕开放式响应展开。过往的自动化研究主要聚焦于定量推演，但大语言模型（LLMs）让定质性兵棋推演的自动化成为可能。我们推出了“Snow Globe”——一个由大语言模型驱动的多智能体系统，用于开展定性兵棋推演。借助Snow Globe，基于文本的定性兵棋推演从场景准备到赛后分析的每个阶段，都可选择由AI、人类或二者协作完成。我们从概念层面描述了其软件架构，并随本文发布了开源实现。作为案例研究，我们模拟了一场关于AI事件响应的桌面推演，以及一场关于地缘政治危机的政治兵棋推演。我们还讨论了该方法的潜在应用，以及它在更广泛的兵棋推演生态系统中的定位。
英文摘要：
Wargames are a powerful tool for understanding and rehearsing real-world decision making. Automated play of wargames using artificial intelligence (AI) enables possibilities beyond those of human-conducted games, such as playing the game many times over to see a range of possible outcomes. There are two categories of wargames: quantitative games, with discrete types of moves, and qualitative games, which revolve around open-ended responses. Historically, automation efforts have focused on quantitative games, but large language models (LLMs) make it possible to automate qualitative wargames. We introduce "Snow Globe," an LLM-powered multi-agent system for playing qualitative wargames. With Snow Globe, every stage of a text-based qualitative wargame from scenario preparation to post-game analysis can be optionally carried out by AI, humans, or a combination thereof. We describe its software architecture conceptually and release an open-source implementation alongside this publication. As case studies, we simulate a tabletop exercise about an AI incident response and a political wargame about a geopolitical crisis. We discuss potential applications of the approach and how it fits into the broader wargaming ecosystem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
