A New Machine Learning Framework for Air Combat Intelligent Virtual Opponent

Abstract

In air combat simulation training systems, it is possible to train with "smart" opponents, which can greatly improve the pilot's combat level. In the new generation of aircraft, an embedded virtual opponent training platform is also designed to enable real-time train with pilots. The core of training is in air combat training. Computers can make decisions based on the current situation. The idea of this paper is to allow pilots to training on specific tactics of warfare in the "human-in-the-loop" combat simulation system, forming strategies under different situation, and then establishing the relationship between machine learning input and output. Through the structured processing of simulation data, the radial neural network model is used for learning and training, and the trained model is used to predict and process the input situation data in real time, and a virtual opponent strategy is generated.