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一种基于Monte Carlo滤波的对POMDPRS系统性能的改进

Performance Promotion for POMDPRS Based on Monte Carlo Filter
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摘要 规划是人工智能研究的一个重要方向,具有极其广泛的应用背景.POMDPRS是一种结合了PRS的持续规划机制、POMDP的概率分布信念模型和极大效用原理的持续规划系统.它具有较强的对动态不确定性环境的适应能力.但是在大状态空间下的信念更新是其作为实时系统的瓶颈.该文试图将Monte Carlo滤波引入POMDPRS,从而达到降低信念更新的复杂度的目的,满足系统实时性的要求. Planning is a main research direction in artificial intelligence and has widely application background. POMDPRS is a continual planning system which combines the continual planning mechanism of PRS, the probabilistic distribution belief model and the maximum utility principle of POMDP, so that it gains stronger abilities of adapting to dynamic nondeterministic environments. However, belief updating is the bottleneck of planning performance in big state space. This paper introduces the Monte Carlo filter into POMDPRS to reduce the complexity of its belief updating, so that it can meet the requirement as a real-time system.
作者 李响
机构地区 中兴通讯南京研发中心
出处 《计算机学报》 EI CSCD 北大核心 2007年第6期999-1004,共6页 Chinese Journal of Computers
关键词 POMDPRS 信念更新 MONTE Carlo滤波 POMDPORS belief update Monte Carlo filter
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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计算机学报
2007年 第6期

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