A Review of The Development of Distributed Task Planning in Command and Control Domain

With the increasing complexity of modern battlefield environment, the continuous improvement of spatial dimension, and the continuous update of weapons and equipment, "decentralization" and "distribution" have become the research hotspots in the domain of command and control. Aiming at the shortcomings of traditional distributed task planning in the domain of command and control, including weak self-learning ability, poor dynamic adjustment ability, and difficulty in adapting to the contemporary battlefield environment, in this paper, combined with the latest research status, the application of artificial intelligence technology including distributed artificial intelligence, reinforcement learning and deep learning in the domain of task planning is introduced, and the principle and limitation of several algorithms are briefly expounded. Inspired by the previous research, a vision for future distributed task planning is also proposed.