这项工作的目的是提供一个交战决策支持工具，超视距（BVR）空战的背景下，防御性反空（DCA）任务。在超视距空战中，交战决策是指飞行员采取进攻姿态并执行相应机动动作来选择与目标交战的时刻。为了对这一决策进行建模，我们使用了巴西空军的航空航天仿真环境（葡萄牙语为Ambiente de Simulação Aeroespacial - ASA），该环境生成了3，729次建设性仿真，每次持续12分钟，总共10，316次交战。我们通过称为DCA指数的操作指标分析了所有样本，DCA指数基于主题专家的经验代表了此类任务的成功程度。这个指标考虑了同一队和对方队的飞机的距离，战斗空中巡逻的点，以及使用的导弹数量。通过在开始之前定义参与状态以及整个参与过程中DCA指数的平均值，我们创建了一个监督学习模型来确定新参与的质量。基于决策树的算法与XGBoost库一起工作，提供了一个回归模型来预测DCA指数，其决定系数接近0.8，均方根误差为0.05，可以为BVR飞行员提供参数以决定是否参与。因此，使用通过模拟获得的数据，这项工作的贡献，通过建立一个决策支持系统的基础上机器学习超视距空战。
英文摘要：
This work aims to provide an engagement decision support tool for Beyond Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA) missions. In BVR air combat, engagement decision refers to the choice of the moment the pilot engages a target by assuming an offensive stance and executing corresponding maneuvers. To model this decision, we use the Brazilian Air Force's Aerospace Simulation Environment (Ambiente de Simulação Aeroespacial - ASA in Portuguese), which generated 3,729 constructive simulations lasting 12 minutes each and a total of 10,316 engagements. We analyzed all samples by an operational metric called the DCA index, which represents, based on the experience of subject matter experts, the degree of success in this type of mission. This metric considers the distances of the aircraft of the same team and the opposite team, the point of Combat Air Patrol, and the number of missiles used. By defining the engagement status right before it starts and the average of the DCA index throughout the engagement, we create a supervised learning model to determine the quality of a new engagement. An algorithm based on decision trees, working with the XGBoost library, provides a regression model to predict the DCA index with a coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05 that can furnish parameters to the BVR pilot to decide whether or not to engage. Thus, using data obtained through simulations, this work contributes by building a decision support system based on machine learning for BVR air combat.
Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shenyang Aircraft Design and Research Institute (SADRI)（沈阳飞机设计研究所）
DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, CASIA（中国科学院自动化研究所复杂系统认知与决策智能重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering
Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)（达勒·莫尔人工智能研究所（IDSIA））；USI-SUPSI（瑞士意大利语区大学-瑞士意大利语区应用科学与艺术大学）；Armasuisse Science + Technology（瑞士联邦国防采购局科技中心）
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics
Dalle Molle Institute for Artificial Intelligence (IDSIA) - SUPSI/USI（达勒莫尔人工智能研究所（IDSIA）- 瑞士南部应用科学与艺术大学/瑞士意大利语区大学）；armasuisse Science and Technology（瑞士联邦国防采购局科学与技术）
