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

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第3期10卷 549-561页

作者： You, Yao Li, Jie Shen, Lincheng Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha Hunan Peoples R China

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.

关键词： Bayesian network (BN) Parameters learning Scarce data cpel algorithm Autonomous mission decision-making

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A constrained parameter evolutionary learning algorithm for Bayesian network under incomplete and small data 36

A constrained parameter evolutionary learning algorithm for ...

引用

第36届中国控制会议

作者： Yao You Jie Li Ning Xu College of Mechatronic Engineering and Automation National University of Defense Technology

ISBN: (纸本)9781538629185

Lack of relevant data is a major challenge for Bayesian network（BN） parameters learning. For the issue, this paper proposes a constrained parameter evolutionary learning algorithm（cpel） which is based on the qualitative knowledge and evolutionary strategy. In detail, firstly qualitative knowledge is employed into BN parameters learning process to reduce the parameter search space where two types of qualitative 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. So combining their advantages will have an important significance for BN parameters learning under incomplete and small data. Comparative experiments show that the cpel algorithm is better than classical EM algorithm in accuracy and timeliness performance, which verify the feasibility and superiority of the algorithm proposed. Additionally, the cpel algorithm has been applied to UAV threat assessment under complex dynamic environment.

关键词： Bayesian network parameters learning cpel algorithm qualitative knowledge evolutionary strategy

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