Bayesian Networks for Intelligent Decision of Airborne Weapon System
Bo Li, Gao Xiao-guang · 2006
According to the relationship of Bayesian networks' nodes, the nodes are divided into deterministic node, non-deterministic node and incomplete deterministic node, so the number of conditional probability required for the node is reduced evidently through corresponding pattern. The knowledge representation of intelligent decision of weapon system using the Bayesian networks is analyzed, and the steps on constructing Bayesian networks for the intelligent decision are presented: choosing the network's nodes, specifying the node's states, linking the nodes, allocating the node's probability. The process of how to use the Bayesian networks for the intelligent decision of weapon system is showed by an example, and the simulation results shows that the decision scheme reasoning from the Bayesian networks is consistent with the application criterion of the airborne weapon system