中文摘要：

针对部分可观察马尔可夫决策过程（POMDP）的信念状态空间规模“维数灾”问题，根据信念状态变量存在可分解和独立关系的特性，提出一种基于动态贝叶斯网络（DBN）的可分解信念状态空间压缩算法（factored belief states space compression, FBSSC）．该算法通过构建变量间依赖关系图，根据独立关系检验去除多余边，将转移函数联合概率分解成若干个条件概率的乘积，实现信念状态空间的无损压缩．对比实验和RoboCupRescue仿真结果表明，本文算法具有较低误差率、较高收敛性和普遍适用性等特性．

英文摘要：

For the dimensionality curse problem of belief state space scale of partially observable Markov decision pro- cess （POMDP）, a factored belief states space compression （FBSSC） algorithm based on dynamic Bayesian network （DBN） is proposed according to the decomposable features and dependent relationship of the belief state variables. Based on the building of the graph of dependent relationship among variables, the algorithm removes the redundant edges by detecting the dependent relationships, and decomposes the joint probability of transition function into the product of several conditional probabilities, which realizes the lossless compression of belief states space. Comparison experiments and RoboCupRes. cue simulation results show that the algorithm has the characteristics of lower error rate, higher convergence, and general applicability.