Military air battle managers face several challenges when directing operations during quickly evolving combat scenarios. These scenarios require rapid assignment decisions to engage moving targets having dynamic flight paths. In defensive operations, the success of a sequence of air battle management decisions is reflected by the friendly force’s ability to maintain air superiority and defend friendly assets. We develop a Markov decision process (MDP) model of a stochastic dynamic assignment problem, named the Air Battle Management Problem (ABMP), wherein a set of unmanned combat aerial vehicles (UCAV) must defend an asset from cruise missiles arriving stochastically over time. Attaining an exact solution using traditional dynamic programming techniques is computationally intractable. Hence, we utilize an approximate dynamic programming (ADP) technique known as approximate policy iteration with least squares temporal differences (API-LSTD) learning to find high-quality solutions to the ABMP. We create a simulation environment in conjunction with a generic yet representative combat scenario to illustrate how the ADP solution compares in quality to a reasonable, closest-intercept benchmark policy. Our API-LSTD policy improves mean success rate by 2.8% compared to the benchmark policy and offers an 81.7% increase in the frequency with which the policy performs perfectly. Moreover, we find the increased success rate of the ADP policy is, on average, equivalent to the success rate attained by the benchmark policy when using a 20% faster UCAV. These results inform military force management and defense acquisition decisions and aid in the development of more effective tactics, techniques, and procedures.

在快速变化的战斗场景中指挥作战时，军事空战管理人员面临多项挑战。这些场景需要快速分配决策来攻击具有动态飞行路径的移动目标。在防御行动中，一系列空战管理决策的成功体现在友军保持空中优势和保卫友军资产的能力上。我们开发了一个名为空战管理问题 (ABMP) 的随机动态分配问题的马尔可夫决策过程 (MDP) 模型，其中一组无人机 (UCAV) 必须保护资产免受随时间随机到达的巡航导弹的攻击。使用传统的动态规划技术获得精确解在计算上是难以处理的。因此，我们利用称为具有最小二乘时间差的近似策略迭代 (API-LSTD) 学习的近似动态规划 (ADP) 技术来找到 ABMP 的高质量解决方案。我们创建了一个结合通用但具有代表性的战斗场景的模拟环境，以说明 ADP 解决方案在质量上如何与合理的、最近的拦截基准策略进行比较。与基准策略相比，我们的 API-LSTD 策略将平均成功率提高了 2.8%，并且该策略完美执行的频率提高了 81.7%。此外，我们发现 ADP 策略成功率的提高平均相当于基准策略在使用快 20% 的 UCAV 时获得的成功率。