Mission planning for joint operations based on machine learning

Abstract

The complexity of modern war is increasing, and the rapid operational mission planning is of great importance to improve the efficiency of command and control. This paper presents a joint operational Task Matrix (TM) model, which is a theoretical method for mission planning. A belief network model is put forward to describe the relationship among the elements in TM model. A naive bayesian learning method for belief network is designed. A mechanism of imagination is put forward to speed up the learning process. A search algorithm named Deep Minimum Threat Generation Tree(DMTGT) is proposed, which can efficiently calculate task priority by balancing search error and search speed. Finally, the validity of above models and algorithms is verified by simulation experiments.

Keywords

• belief network|machine learning|operation planning|situation awareness