Abstract: Cooperative and adversarial path planning is a significant issue in scenarios such as air combat and sport games. The challenge is the adaptation for dynamic feedback and the cooperation between multi-agents. A neural network and artificial potential field based method is proposed, in which the potential gain coefficient of the artificial potential field is adaptively adjusted by the Back Propagation (BP) neural network. The artificial potential field can be seen as feature extraction for the neural network on its output phase. To face the issue of insufficient natural samples for training the neural network, the sample is generated by simulation and optimized by a genetic algorithm and receding horizon optimization. The "different of distance" and "different of heading" are defined to show the characters of cooperative and adversarial path planning, and the black box feature and learning capacity of neural network are well exploited for cooperative and adversarial path planning. This method is evaluated in a two on one anti-stealth beyond-visual-range air combat, and shows significant improvement of performance and affordable costs. The computational complex analysis shows that our algorithm is scalable for multi-aircrafts cases.

Key words: cooperative and adversarial path planning, simulated sample generation, neural network, artificial potential field, genetic algorithm, receding horizon optimization, beyond-visual-range air combat, anti-stealth