基于人工智能平台的战术战棋实验

D. Tarraf, J. Gilmore, D. Barnett, Scott S. Boston, David Frelinger, Daniel C. Gonzales, Alexander C. Hou, Peter Whitehead
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引用次数: 2

摘要

在这份报告中,研究人员实验了如何将人工智能/机器学习(AI/ML)功能整合到战争游戏中。我们修改并增强了商业桌面战争游戏中使用的规则和交战统计数据,以启用(1)远程操作和完全自主的战斗车辆和(2)具有AI/ ml支持的态势感知的车辆,以显示这两种类型的车辆在蓝军(美国)和红军(俄罗斯)部队之间的连队级交战中如何表现。我们为这场战争游戏开发的增强规则和统计数据部分基于美国陆军开发和部署机器人和人工智能/机器学习武器和其他系统的不断发展的计划。然而,我们还描绘了具有自主探测、识别和攻击目标能力的战斗车辆,而无需人为干预,这是陆军目前没有设想的。我们制定的规则试图真实地描绘AI/ ml支持系统的能力和局限性,包括它们对选定敌人对策(如干扰)的脆弱性。未来的工作可以提高AI/ ml支持系统的游戏玩法和表现的现实性,从而为美国国防部的采办和运营社区提供有用的信息。
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An Experiment in Tactical Wargaming with Platforms Enabled by Artificial Intelligence
In this report, researchers experimented with how postulated artificial intelligence/machine learning (AI/ML) capabilities could be incorporated into a wargame. We modified and augmented the rules and engagement statistics used in a commercial tabletop wargame to enable (1) remotely operated and fully autonomous combat vehicles and (2) vehicles with AI/ML-enabled situational awareness to show how the two types of vehicles would perform in company-level engagement between Blue (US) and Red (Russian) forces. The augmented rules and statistics we developed for this wargame were based in part on the US Army’s evolving plans for developing and fielding robotic and AI/ML-enabled weapon and other systems. However, we also portrayed combat vehicles with the capability to autonomously detect, identify, and engage targets without human intervention, which the Army does not presently envision. The rules we developed sought to realistically portray the capabilities and limitations of AI/ML-enabled systems, including their vulnerability to selected enemy countermeasures, such as jamming. Future work could improve the realism of both the gameplay and representation of AI/ML-enabled systems, thereby providing useful information to the acquisition and operational communities in the US Department of Defense.
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来源期刊
CiteScore
2.80
自引率
12.50%
发文量
40
期刊最新文献
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