Dynamic task allocation for formation air-to-ground attack

In accordance with the allocation of random arriving tasks, queuing network is proposed and then utilized to establish combat model for air-to-ground attack of formation. With the methodology of Markov Decision Processes (MDP), the paper establishes the dynamic task assignment model under the complete information condition. Allowing for the issue of dynamic task allocation with incomplete information, load threshold and value function sharing mechanism are proposed. Afterwards, two layers Q-learning algorithm is presented in this context to establish the dynamic task allocation model, which aims to achieve the cooperation with in formation and between formations. The simulation result indicates that the model aforementioned can enhance the long-term profit value, as well as avoid omitting the target with bigger income. Furthermore, it sheds light on the study on the dynamic task allocation and combat effectiveness evaluation for air-to-ground attack of formation problem in theory and method.