Abstract
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.
Abstract (translated)
随着机器学习 recent 进步，创建在模拟空中战斗中表现真实的代理已成为一个增长兴趣的领域。本调查探讨了使用机器学习技术对建模空中战斗行为的应用，这是为了增强基于模拟的飞行员培训的可能性。当前的模拟实体往往缺乏现实行为，而传统的行为建模需要大量的人力劳动，并且在开发过程中容易丢失关键领域知识。机器学习算法中的强化学习和模仿学习的进步已经证明了，代理可以从数据中学习复杂的行为，这可能会比手动方法更快、更具有可扩展性。然而，使自适应代理能够执行战术机动和操作武器和传感器仍然是一个重大挑战。调查检查了应用、行为模型类型、普遍的机器学习方法和开发过程中的人力和技术挑战。另一个挑战是将自己从学习环境中转移到军事仿真系统，以及随之而来的标准化需求。关于增加对超视距场景的重视、多代理机学习与合作、使用层次行为模型和标准化及研究合作倡议，提出了四个主要建议。这些建议旨在解决当前问题，并指导开发更全面、可扩展和真实感的机器学习为基础的行为模型，为军事应用做好准备。
URL
https://arxiv.org/abs/2404.13954
