An effective Bayesian network parameters learning algorithm for autonomous mission decision-making under scarce data

In this paper, a constrained parameter evolutionary learning (CPEL) algorithm for Bayesian network (BN) parameters learning under scarce data is proposed, which can be applied to UAV autonomous mission decision-making. In detail, firstly qualitative domain knowledge is employed into BN parameters learning process to reduce the parameter search space where two types of qualitative domain knowledge with experts’ confidence are presented; and then evolutionary strategy is introduced into the process to avoid the problem that classical learning technique falls into local optimum easily in which the special encoding for the BN parameters is presented and some evolutionary strategies are discussed. Moreover, the global convergence of the proposed algorithm is proven. According to numerical experiments, it’s demonstrated that the CPEL algorithm has better accuracy and timeliness performance than the classical EM algorithm under the same condition. Additionally, a case study of the proposed algorithm in UAV autonomous mission decision-making has been conducted, showing that CPEL algorithm can satisfy the need of UAV autonomous mission decision-making in a complex dynamic environment.

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