信息与控制2012，Vol.41Issue(6)：713-719,7.DOI:10.3724/SP.J.1219.2012.00713
基于动态贝叶斯网络的可分解信念状态空间压缩算法
Factored Belief States Space Compression Algorithm Based on Dynamic Bayesian Network
摘要
Abstract
For the dimensionality curse problem of belief state space scale of partially observable Markov decision process (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.关键词
马尔可夫决策过程/动态贝叶斯网络/维数灾/信念状态空间/条件独立Key words
MDP (Markov decision process)/ DBN (dynamic Bayesian network)/ curse of dimensionality/ belief states space/ conditional independence分类
信息技术与安全科学引用本文复制引用
仵博,吴敏,郑红燕,冯延蓬..基于动态贝叶斯网络的可分解信念状态空间压缩算法[J].信息与控制,2012,41(6):713-719,7.基金项目
国家自然科学基金资助项目(61074058,60874042) （61074058,60874042）
广东省自然科学基金资助项目(S2011040004769). （S2011040004769）
