Improved Bayesian Network and Its Application in Autonomous Capability Evaluation

Abstract

The Bayesian network (BN) is a new intelligent evaluation technology, which has become one of the effective methods to solve uncertainty problems and deal with asymmetric information. However, its application is limited in the case of complex indicator systems and interrelated variables. To solve these problems, an improved BN model based on game theory is proposed. First, an IHORAFA attribute reduction algorithm is used to optimize the index system. Then, a weighted BN evaluation model is proposed for the problem of correlation among indicators, which uses the improved combination weighting method of game theory to determine the optimal weight and improve the accuracy of weight calculation. Finally, the improved BN is applied to the autonomous capability evaluation of ground attack UAVs. The simulation results show that the improved BN model can be used for assessment and reasoning under uncertain conditions and variable correlation.