  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.
