• Article
  • | |
  • Metrics
  • |
  • Reference [22]
  • |
  • Related [20]
  • |
  • Cited by [5]
  • | |
  • Comments
    Abstract:

    How to decrease the observation variables for strong planning under partial observation is explored. Beginning from a domain under no observation, add necessary observation variables gradually to get a minimal set of observation variables necessary. Two methods are presented to decrease observation variables. With the former, when any of the two distinct states of the domain can be distinguished by an observation variable, this algorithm can find a minimal set of observation variables necessary for the execution of a plan. With the latter, when there are states that can’t be distinguished by only one observation variable, this algorithm can find a set of observation variables as small as possible which are necessary for the execution of a plan.

    Reference
    [1] Weld DS. Recent advances in AI planning. AI Magazine, 1999,20(2):93-123.
    [2] Bertoli P, Cimatti A, Roveri M, Traverso P. Planning in nondeterministic domains under partial observability via symbolic model checking. In: Nebel B, ed. Proc. of the IJCAI. Seattle: Morgan Kaufmann Publishers, 2001. 473-478.
    [3] Bertoli P, Cimatti A, Roveri M, Traverso P. Strong planning under partial observability. Artificial Intelligence, 2006,170(4-5): 337-384.
    [4] Bertoli P, Cimatti A, Traverso P. Interleaving execution and planning for nondeterministic, partially observable domains. In: de Mántaras RL, Saitta L, eds. Proc. of the ECAI. Amsterdam: IOS Press, 2004. 657-661.
    [5] Herzig A, Lang J, Marquis P. Action representation and partially observable planning using epistemic logic. In: Gottlob G, Walsh T, eds. Proc. of the Int’l Joint Conf. on Artificial Intelligence (IJCAI). Morgan Kaufmann Publishers, 2003. 1067-1072.
    [6] Poupart P, Boutilier C. Vector-Space analysis of belief-state approximation for POMDPs. In: Nebel B, ed. Proc. of the Conf. on Uncertainty in Artificial Intelligence (IJCAI). Seattle: Morgan Kaufmann Publishers, 2001. 445-452.
    [7] Huang W, Wen ZH, Jiang YF, Wu LH. Observation reduction for strong plans. In: Veloso MM, ed. Proc. of the Int’l Joint Conf. on Artificial Intelligence (IJCAI). Menlo Park: AAAI Press, 2007. 1930-1935.
    [8] Bertoli P, Cimatti A, Roveri M. Heuristic search+symbolic model checking=efficient conformant planning. In: Nebel B, ed. Proc. of the 7th IJCAI. Seattle: Morgan Kaufmann Publishers, 2001. 467-472.
    [9] Cimatti A, Roveri M, Bertoli P. Conformant planning via symbolic model checking and heuristic search. Artificial Intelligence, 2004,159(1-2):127-206.
    [10] Cimatti A, Roveri M, Traverso P. Strong planning in non-deterministic domains via model checking. In: Simmons RG, Veloso MM, Smith S, eds. Proc. of the 4th Int’l Conf. on AI Planning Systems. Menlo Park: AAAI Press, 1998. 36-43.
    [11] Cimatti A, Pistore M, Roveri M, Traverso P. Weak, strong, and strong cyclic planning via symbolic model checking. Artificial Intelligence, 2003,147(1-2):35-84.
    [12] Cimatti A, Roveri M. Conformant planning via symbolic model checking. Artificial Intelligence Research, 2000,13(1):305-338.
    [13] Bonet B, Geffner H. Planning with incomplete information as heuristic search in belief space. In: Chien S, Kambhampati S, Knoblock CA, eds. Proc. of the 5th Int’l Conf. on Artificial Intelligence Planning and Scheduling. Menlo Park: AAAI Press, 2000. 52-61.
    [14] Hoffman J, Brafman R. Contingent planning via heuristic forward search with implicit belief states. In: Biundo S, Myers KL, Rajan K, eds. Proc. of the ICAPS. Menlo Park: AAAI Press, 2005. 71-80.
    [15] Wu KH, Jiang YF. Planning with domain constraints based on model-checking. Journal of Software, 2004,15(11):1629?1640 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/15/1629.htm
    [16] Bertoli P, Cimatti A, Roveri M. Conditional planning under partial observability as heuristic-symbolic search in belief space. In: Cesta A, ed. Proc. of the ECP. Berlin, Heidelberg: Springer-Verlag, 2001. 379-384.
    [17] Hoffman J, Braftman R. Conformant planning via heuristic forward search: A new approach. Artificial Intelligence, 2006,170(6-7): 507-541.
    [18] Yin MH, Lin H, Sun JG, Wang JA. Extension or resolution: A novel approach for reasoning in possibilistic logic. In: Melin P, Castillo O, Gómez-Ramírez E, Kacprzyk J, Pedrycz W, eds. Proc. of the IFSA. Berlin, Heidelberg: Springer-Verlag, 2007. 354-362.
    [19] Yin MH, Sun JG, Cai DB, Lu S. A novel framework for plan recognition: Planning graph as a basis. In: Veloso MM, ed. Proc. of the IJCAI WK on NRAC. Menlo Park: AAAI Press, 2007. 472-476.
    [20] Yin MH, Lin H, Sun JG. Counting models using extension rules. In: Collins J, Faratin P, Parsons S, Rodríguez-Aguilar JA, Sadeh NM, Shehory O, Sklar E, eds. Proc. of the AAAI. Menlo Park: AAAI Press, 2007. 1390-1395.
    [21] Domshlak C, Hoffmann J. Fast probabilistic planning through weighted model counting. In: Long D, Smith SF, Borrajo D, McCluskey L, eds. Proc. of the ICAPS. Menlo Park: AAAI Press, 2006. 1655-1664. 附中文参考文献:
    [15] 吴康恒,姜云飞.基于模型检测的领域约束规划.软件学报,2004,15(11):1629-1640. http://www.jos.org.cn/1000-9825/15/1629.htm
    Comments
    Comments
    分享到微博
    Submit
Get Citation

周俊萍,殷明浩,谷文祥,孙吉贵.部分可观察强规划中约减观察变量的研究.软件学报,2009,20(2):290-304

Copy
Share
Article Metrics
  • Abstract:4314
  • PDF: 6612
  • HTML: 0
  • Cited by: 0
History
  • Received:March 07,2007
  • Revised:September 04,2007
You are the first2033282Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063