Carrier-borne aircrafts aviation operation automated scheduling using multiplicative weights apprenticeship learning

Abstract

Efficiency and safety are vital for aviation operations in order to improve the combat capacity of aircraft carrier. In this article, the theory of apprenticeship learning, as a kind of artificial intelligence technology, is applied to constructing the method of automated scheduling. First, with the use of Markov decision process frame, the simulative model of aircrafts launching and recovery was established. Second, the multiplicative weights apprenticeship learning algorithm was applied to creating the optimized scheduling policy. In the situation with an expert to learn from, the learned policy matches quite well with the expert’s demonstration and the total deviations can be limited within 3%. Finally, in the situation without expert’s demonstration, the policy generated by multiplicative weights apprenticeship learning algorithm shows an obvious superiority compared to the three human experts. The results of different operation situations show that the method is highly robust and well functional.