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模型检测规划中的状态分层方法 被引量：14

Method of Hierarchical States in Planning Based on Model Checking
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摘要 基于模型检测的规划方法是最近发展起来的新方法,它可以处理带有不确定性的规划问题.分别设计了对求弱规划解、强规划解和强循环规划解的问题中的状态进行分层的方法.状态被分层后,求规划解只需要在从上层到其下一层状态之间寻找状态动作序偶就可以了,其他状态动作序偶都可以去掉.分别获得了求弱规划解、强规划解和强循环规划解时状态被分层后的一些重要性质,这些性质是关于一些状态动作序偶是否可以不参与构成弱规划解、强规划解和强循环规划解的结论.通过所获得的性质可以将大量的状态动作序偶直接去掉,从而减少问题规模.以往的对基于模型检测规划的研究都是采用从目标状态开始的反向搜索方法,在状态被分层以后可以采用正向搜索技术展开相应的研究. Planning by model checking is an approach to planning under uncertainty that deals with nondeterminism. Three ways which obtain hierarchical states for searching weak planning, strong planning, and strong cyclic planning are respectively designed. Based on hierarchical states, some important conclusions on a weak solution, a strong solution, and a strong cyclic solution are obtained. What can be eliminated directly are all found when a weak solution, a strong solution, and a strong cyclic solution are in turn searched. Therefore many state-action pairs can be eliminated directly before starting planning. In fact, a way has been given which is based on a search proceeding forwards from the initial states towards the goal states.
作者 文中华 黄巍 刘任任 姜云飞
机构地区 湘潭大学信息工程学院 华南理工大学计算机科学与工程学院 中山大学软件研究所
出处 《软件学报》 EI CSCD 北大核心 2009年第4期858-869,共12页 Journal of Software
基金 国家自然科学基金Nos.60673193,60773047,60773201 湖南省重点学科建设项目No.081202 湖南省教育厅科研项目No.08C874 湘潭大学校基金No.kz08009~~
关键词 模型检测 状态分层 不确定规划 正向搜索 状态动作序偶 model checking hierarchical state planning under uncertainty forward search state-action pair
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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参考文献15

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同被引文献98

  • 1谢希仁.计算机网络(第五版)[M].北京:电子工业出版社.2007.5.
  • 2W Huang, Z H Wen, Y F Jiang, A X Chen. Comparison between two languages used to express planning goals: CTL and EAGLE [ C]. Proc. of PRICAI' 06. Guilin, China: Springer - Verlag, 2006 : 180-189.
  • 3Cimatti M Roveri. Conformant planning via model checking and heuristic search [ J]. Artificial Intelligence, 2004, 159 ( 1-2 ) : 127-206.
  • 4J Rintanen. Backward plan construction under partial observability [ C ]. Proc. Of the 6th Int' 1 Conf. AAAI Press, 2002:173-182.
  • 5P Bertoli, A Cimatti, M Roveri and P traverse. Strong planning under partial observability [ J ]. Artificial Intelligence, 2006, 170 : 337 -384.
  • 6Cimatti M Roveri, P Traverso. Automatic OBDD-based generation of universal plans in non - deterministic domains [ C ]. Proc. of AAAI' 98. Madison, USA: AAAI Press, 1998 : 875-881.
  • 7Cimatti M Roveri. Conformant planning via symbolic model chec- king[ J ]. Journal of Artificial Intelligence Research, 2000, 13 : 305 -338.
  • 8A Cimatti, M Pistore, M Rovveri, P Traverso. Weak strong and strong cyclic planning via symbolic model checking[J]. Artificial Intelligence, 2003,147 ( 1-2 ) :35-84.
  • 9W Huang, H Peng. Structured plan and its execution for extended goals[J]. Journal of Chongqing University of Posts and Telecom- munications. 2009:253-261.
  • 10W Huang , Z H Wen , Y F Jiang, H Peng. Structured Plans and Observation Reduction for Plans with Contexts [ C ]. Proc. of IJ- CAI' 09. AAAI Press, 2009 : 1721 - 1727.

引证文献14

二级引证文献11

软件学报
2009年 第4期

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