With the recent advances in machine learning, creating agents that behave realistically in simulated air combat has become a growing field of interest. This survey explores the application of machine learning techniques for modeling air combat behavior, motivated by the potential to enhance simulation-based pilot training. Current simulated entities tend to lack realistic behavior, and traditional behavior modeling is labor-intensive and prone to loss of essential domain knowledge between development steps. Advancements in reinforcement learning and imitation learning algorithms have demonstrated that agents may learn complex behavior from data, which could be faster and more scalable than manual methods. Yet, making adaptive agents capable of performing tactical maneuvers and operating weapons and sensors still poses a significant challenge. The survey examines applications, behavior model types, prevalent machine learning methods, and the technical and human challenges in developing adaptive and realistically behaving agents. Another challenge is the transfer of agents from learning environments to military simulation systems and the consequent demand for standardization. Four primary recommendations are presented regarding increased emphasis on beyond-visual-range scenarios, multi-agent machine learning and cooperation, utilization of hierarchical behavior models, and initiatives for standardization and research collaboration. These recommendations aim to address current issues and guide the development of more comprehensive, adaptable, and realistic machine learning-based behavior models for air combat applications.

翻译：随着机器学习的最新进展，在模拟空战中创建具有逼真行为表现的人工智能体已成为一个日益受到关注的研究领域。本综述受其提升基于模拟的飞行员训练潜力的驱动，探讨了机器学习技术在空战行为建模中的应用。当前模拟实体往往缺乏逼真的行为表现，且传统行为建模方式劳动密集，并容易在开发步骤间丢失关键领域知识。强化学习与模仿学习算法的进步已证明，智能体能够从数据中习得复杂行为，其速度与可扩展性优于人工方法。然而，构建能够执行战术机动、操作武器与传感器的自适应智能体仍是一项重大挑战。本综述考察了相关应用、行为模型类型、主流机器学习方法，以及开发自适应且行为逼真的智能体所面临的技术与人文挑战。另一个挑战在于将智能体从学习环境迁移至军事仿真系统，以及由此产生的标准化需求。本文提出了四项主要建议：强化超视距场景研究、推进多智能体机器学习与协作、采用分层行为模型，以及倡导标准化与科研合作举措。这些建议旨在解决当前问题，并指导开发更全面、更适应、更逼真的空战应用机器学习行为模型。