登录
注册
您所在的位置:首页>>报告>>报告详情

Exploring the Feasibility and Utility of Machine Learning-Assiste...

探索机器学习辅助指挥和控制的可行性和实用性:第1卷,发现和建议

【作       者】:

Matthew Walsh, Lance Menthe, E...

【机       构】: 兰德机构
【承研机构】:

【英文机构】: RAND
【原文地址】: https://www.rand.org/pubs/research_reports/RRA263-1.html
【发表时间】:

2021-07-22

摘要

In 2019, the Air Force Research Laboratory, Information Directorate (AFRL/RI) asked RAND Project AIR FORCE (PAF) to examine and recommend opportunities for applying AI to Air Force C2. The research project Exploring the Near-Term Feasibility and Utility of Machine Learning Assisted Operational Planning was conducted in PAF’s Force Modernization program to address this question. A second project was conducted in parallel to examine the separate but related topic of complexity imposition. This report presents the primary result of the study on AI: an analytical framework for understanding the suitability of a particular AI system for a given C2 problem and for evaluating the AI system when applied to the problem. We demonstrate the analytical framework with three technical case studies focused on master air attack planning, sensor management, and personnel recovery. The C2 processes examined in these case studies are central to current and future C2 concepts of operation, and they exemplify the range of characteristics that make C2 problems so challenging.

标签: {{b}}
展开

相关报告