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智能规划的逻辑编码方式研究 被引量:2

Logical Encoding Methods in Intelligent Planning
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摘要 逻辑编码方式的设计和实现是基于转换的规划方法有效处理的关键.对几种智能规划方法中的逻辑编码方式予以分析,分别介绍线性编码、基于Graphplan的编码、基于状态的编码、基于动作的编码、基于命题的编码、基于转移的编码、提升的因果编码、基于多值变元的编码、基于有向二元决策图的编码以及基于约束可满足的编码等,并结合国际规划竞赛和相关论文等的实验结论,说明上述编码方式的有效性和可行性,分析该类编码方式在其他领域的应用前景.最后,提出目前智能规划方法中逻辑编码方式研究所面临的挑战、可能的处理方法,以及与之相关的研究热点与趋势. The design and implementation of logical encoding methods are the key issues of translation based planning methods,which need to translate a given planning problem to a series of other classical solvable problems during planning procedures.All the logical encoding methods need to consider the logical representations and reasonings based on the crossponding propositional logic,first-order logic,multi-value logic,probabilistic logic,modal logic,epistemic logic,or the other adopted non-classical logics.This paper introduces the concrete details of the state-of-the-art logical encoding methods in intelligent planning,which include linear encoding,Graphplan based encoding,state based encoding,action based encoding,proposition based encoding,transition based encoding,lifted casual encoding,multi-value variable based encoding,ordered binary decision diagram based encoding,constraint satisfiabilitiy based encoding and so on.It also introduces the possible needed encoding methods of probabilistics,epistemic properties,modal assumptions,and flexible constraints for planning operations or states of some proposed Abstract planning domain problems,whose formal characteristic expressions are still disputed.After considering experimental results of International Planning Competition and relevant papers,we conclude their corresponding soundness and possibility,and also application prospects in other relevant areas.Finally,we propose the challenges and possible responding methods,and also possible hotspots of them.
出处 《计算机研究与发展》 EI CSCD 北大核心 2012年第3期607-619,共13页 Journal of Computer Research and Development
基金 国家自然科学基金项目(60773097 60873044 60803102 61070084 61100090) 中国博士后科学基金项目(2011M500612) 中央高校基本科研业务费专项基金项目(201103124 200903183) 符号计算与知识工程教育部重点实验室(吉林大学)开放基金项目(93K-17-2009-K02 93K-17-2009-K06)
关键词 智能规划 逻辑 编码方式 编码 公理 intelligent planning logic encoding method encoding axiom
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参考文献49

  • 1McDermott D, Hendler J. Planning: What it is, what it could be, an introduction to the special issue on planning and scheduling [J]. Artificial Intelligence, 1995, 76(1/2): 1-16.
  • 2Ghallab M,Nan D,Traverso P.自动规划:理论和实践[M].姜云飞,杨强,凌应标,译.北京:清华大学出版社,2008.
  • 3吕帅,刘磊,石莲,李莹.基于自动推理技术的智能规划方法[J].软件学报,2009,20(5):1226-1240. 被引量:22
  • 4Bonet B, Geffner H. Planning as heuristic search [J]. Artificial Intelligence, 2001, 129(1): 5-33.
  • 5Fox M, Long D. PDDL2. 1: An extension to PDDL for expressing temporal planning domains [J]. Journal of Artificial Intelligence Research, 2003, 20: 61-124.
  • 6Roberts M, Howe A. Learning from planner performance [J]. Artificial Intelligence, 2009, 173(5/6):536-561.
  • 7Gerevini A E, Haslum P, Long D, et al. Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners [J]. Artificial Intelligence, 2009, 173(5/6):619-668.
  • 8Helmert M. International planning competition [OL]. 2008. [2009-08-13]. http,//ipc, informatik, uni-freiburg, de/.
  • 9Kautz H, Selman B. Planning as satisfiability [C] //Proc of the 10th European Conf on Artificial Intelligence. Chiehester: John Wiley & Sons, 1992:359-363.
  • 10Blum AL, Furst ML. Fast planning through planning graph analysis [J]. Artificial Intelligence, 1997, 90(1/2) : 281-300.

二级参考文献111

共引文献55

同被引文献33

  • 1Ghallab M, Nau D, Traverso P. Automated Planning Theory and Practice[MJ. San Francisco, CA: Morgan Kaufmann, 2004.
  • 2Yang Qiang, Wu Kangheng ,Jiang Yunfei. Learning action models from plan examples using weighted Max-SAT[J]. Artificial Intelligence, 2007, 171(2(3): 107-143.
  • 3Gil Y. Description logics and planning[J]. Al Magazine, 2005,26(2): 73-84.
  • 4McDermott D. PDDL-the planning domain definition language, CVC TR-98-003fDCS TR-1l65[RJ. New Haven: Yale Center for Computational Vision and Control, 1998.
  • 5Gerevini A. Long D. Plan const rairus and preferences in PDDL3: The language of the fifth international planning competition[R]. Brescia, Italy: University of Brescia, 2005.
  • 6Blum A L, Furst M L. Fast planning through planning graph analysis[CJ I/Proc of the 14th IntJoint Conf on Artificial Intelligence (] JCAI'95). San Francisco, CA: Morgan Kaufmann, 1995: 1636-1642.
  • 7Bertoli P, Cimani A, Pistore M, et al. MBP: A model based planner[CJ IIProc of the 17th IntJoint Conf on Artificial Intelligence (] JCAI'OI) Workshop on Planning under Uncertainty and Incomplete Information. San Francisco, CA: Morgan Kaufmann, 2001: 93-97.
  • 8Edelkamp S. Helmert M. MIPS: The model-checking integrated planning system[J]. AI Magazine, 2001, 22(3): 67-71.
  • 9Kissmann P. Edelkamp S. Solving fully-observable non?deterministic planning problems via translation into a general game[GJ I/LNCS 5803: Proc of KI. Berlin: Springer, 2009: 1-8.
  • 10FuJ, Ng V, et al. Simple and fast strong cyclic planning for fully-observable nondeterministic planning problems[CJ II Proc of the 22nd IntJoint Conf on Artificial Intelligence (] JCAI'l]). Menlo Park, CA: AAAI, 2011: 1949-1954.

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