[1]闫书亚,殷明浩,谷文祥,等.概率规划的研究与发展[J].智能系统学报,2008,3(1):9-22.
 YAN Shu-ya,YIN Ming-hao,GU Wen-xiang,et al.Research and advances in probabilistic planning[J].CAAI Transactions on Intelligent Systems,2008,3(1):9-22.
点击复制

概率规划的研究与发展

参考文献/References:
[1] WELD D. Recent advances in AI planning[R]. UWCSE9810 01, 1999.
[2] CIMATTI A, ROVERI M. Conformant planning via symbolic model checking[J] . Journal of Artificial Intelligence Research, 2000, 13: 305338.
[3] CIMATTI A, ROVERI M, BERTOLI P. Conformant planning via symbolic model ch e c king and heuristic search[J]. Artificial Intelligence, 2004, 159 (12):1272 0 6.
[4] HOFFMAN J, BRAFTMAN R. Conformant planning via heuristic forward search: a new approach[J]. Artificial Intelligence, 2006, 170(67): 507541.
[5] SMITH D, WELD D. Conformant graphplan[C]// Proceedings of 15th National C onference on Artificial Intelligence. Madison, Wisconsin, 1998.
[6] WELD D, ANDERSON C, SMITH D. Extending graphplan to handle uncertainty & s en sing actions[C]// Proceedings of 15th National Conference on Artificial Intel li gence. Madison, Wisconsin, 1998. 
[7] HOFFMANN J, BRAFMAN R I. Contingent planning via heuristic forward search wi th implicit belief states[C]// Proceedings of Fifteenth International Confere nc e on Automated Planning & Scheduling.[S.l.]:AAAI Press, 2005.
[8] WAH B W, CHEN Y. Constrained partitioning in penalty formulations for sol v in g temporal planning problems[J]. Artificial Intelligence, 2006, 170(3): 1872 3 1.
[9] CHEN Y, HSU C, WAH B. Temporal planning using subgoal partitioning and re s ol ution in SGplan[J]. Journal of Artificial Intelligence Research, 2006, 26: 323 369.
[10]FOX M, LONG D. PDDL2.1: an extension to PDDL for expressing temporal plan ni ng domains[J]. Journal of Artificial Intelligence Research, 2003, 20: 61124. 
[11]PHILIPPE L. Algorithms for propagating resource constraints in AI plannin g and scheduling: existing approaches and new results[J]. Artificial Intelligenc e, 2003, 143(2):151188.
[12]HANSEN E, ZILBERSTEIN S. LAO*: a heuristic search algorithm that finds so lutions with loops[J]. Artificial Intelligence, 2001, 129(12): 3562.
[13]BONET B, GEFFNER H. Improving the convergence of realtime dynamic program mi ng[C]// Proceedings of 13th International Conference on Automated Planning an d Scheduling(ICAPS). Trento, Italy, 2003.
[14]BONET B, GEFFNER H. An algorithm better than AO*?[C]// Proceedings of A A AI05. Pittsburgh, Pennsylvania: AAAI Press,2005.
[15]MCDERMOTT D. The 1998 AI planning systems competition[J]. AI Magazine, 2000, 21(2): 3555.
[16]GHALLAB M, HOWE A E, KNOBLOCK C A, et al. PDDLthe planning domain defini tion language[R]. CVC TR98003/DCS TR1165, 1998.
[17]YOUNES H, LITTMAN M L, WEISSMAN D, et al. The first probabilistic track o f the international planning competition[J]. Journal of Artificial Intelligence Research, 2005, 24: 851887.
[18]YOUNES H, LITTMAN M. The language for the probabilistic part of IPC4[C ]// Proceedings of International Planning Competition. Whistler, Canada, 2004 .
[19]DEARDEN R, BOUTILIER C. Abstraction and approximate decisiontheoretic pl anning[J]. Artificial Intelligence, 1997, 89(12):219283.
[20]BERTSEKAS D P. Dynamic programming and optimal control[M]. Belmont: Ath ena Scientific, 1995.
[21]BELLMAN R E. Dynamic programming[M]. Princeton: Princeton University Pr ess, 1957.
[22]RUSSELL S, NORVIG P.人工智能——一种现代方法[M].2版.姜 哲, 金奕江, 译. 北京: 人民邮电出版社, 2004.
[23]BERTSEKAS D P. Dynamic programming: deterministic and stochastic models[ M]. New Jersey: PrenticeHall, 1987.
[24]BARTO A, BRADTKE S, SINGH S. Learning to act using realtime dynamic prog ramming[J]. Artificial Intelligence, 1995, 72(12): 81138.
[25]KORF R. Realtime heuristic search[J]. Artificial Intelligence, 1990, 4 2(23): 189211.
[26]GARDNER M. Mathematical games[J]. Sci Amer, 1973, 228(1): 108.
[27]WATKINS C J. Learning from delaye d rewards[ D]. Cambridge: Cambridge University, 1989.
[28]BONET B, GEFFER H. mGPT: a probabilistic planner based on heuristic searc h[J]. Journal of Artificial Intelligence Research, 2005, 24: 933944.
[29]BONET B,GEFFNER H. Faster heuristic search algorithms for planning with un certainty and full feedback[C]// Proceedings of the 18th International Joint Co nference on Artificial Intelligence. Acapulco, Mexico,2003.
[30]TARJAN R E. Depth first search and linear graph algorithms[J]. SIAM Jou rnal on Computing, 1972, 1(2): 146160.
[31]BONET B, GEFFNER H. Learning in depthfirst search: a unified approach to h euristic search in deterministic, nondeterministic, probabilistic, and game tr e e settings[R]. Caracas:Universidad Simon Bolivar, 2005.
[32]BONET B, GEFFNER H. Learning depthfirst search: a unified approach to he ur istic search in deterministic and nondeterministic settings, and its applicati o n to MDPs[C]// Proceedings of 16th International Conference on Automated Plan ni ng and Scheduling. Cumbria, UK, 2006.
[33]BUFFET O, ABERDEEN D. The factored policy gradient planner[C]// Proceed i ngs of the Fifth International Planning Competition. Cumbria, UK , 2006.
[34]MAUSAM, WELD D. Concurrent probabilistic temporal planning[C]// Interna t iona l Conference on Automated Planning and Scheduling (ICAPS). Monterey, CA, US A, 2005.
[35]BAXTER J, BARTLETT P, WEAVER L. Experiments with infinitehorizon[J]. J ournal of Artificial Intelligence Research, 2001, 15: 351381.
[36]YOUNES H L S, LITTMAN M L. An extension to PDDL for expressing planning d om ains with probabilistic effects[R]. CMUCS04167, Carnegi e M ellon University, 2004.
[37]FENG Z, HANSEN E. Symbolic heuristic search for factored Markov decis ion pr ocesses[C]// Proceedings of the 18th National Conference on Artificial Intell ig ence (AAAI02). Edmonton, Alberta, Canada, 2002.
[38]BLUM A, FURST M. Fast planning through planning graph analysis[J]. Arti ficial Intelligence, 1997, 90(1): 281300.
[39]YANG Q, WU K H, JIANG Y F. Learning action models from plan examples usin g weighted MAXSAT[J]. Artificial Intelligence, 2007, 171(23):107143.
[40]HUANG W, WEN Z H, JIANG Y F, et al. Observation reduction for strong pla ns [C]// Proceedings of the 20th International Joint Conference on Artificial In te lligence. Hyderabad, India, 2007.
[41]陈蔼祥, 姜云飞, 张学农,等. GP基于规划图的遗传规划算法[J]. 计算机学报, 2007, 30 (1): 153160.
?CHEN Aixiang JIANG Yunfei, ZHANG Xuenong, et al. GP: genetic planning algori thm based on planning graph[J].Chinese Journal of Computers, 2007, 30(1):153 1 60.
[42]SMITH D E, WELD D. Conformant graphplan[C]// Proceedings of 15t h National Co nference on Artificial Intelligence. Madison, Wisconsin, USA, 199 8.
[43]BLUM A, LANGFORD J. Probabilistic planning in the graphplan framework[C ]// P roceedings of the Fifth European Conference on Planning. Durham, UK, 19 99.
[44]GU W X, OU H J, LIU R X, et al. An improved probabilistic planning algori th m based on pgraphplan[C]// Proceedings of the Third International Conference on Machine Learning and Cybernetics. Shanghai, 2004.
[45]LITTLE I, BAUX S. Concurrent probabilistic planning in the graphplan fr amework[C]// The 16th International Conference on Automated Planning and Sche du ling(ICAPS). Cumbria, UK, 2006.
[46]SANNER S, BOUTILIER C. Probabilistic planning via linear valueapproximat io n of firstorder MDPs[C]// The 16th International Conference on Automated Pl an n ing and Scheduling(ICAPS). Cumbria,U K, 2006.
[47]REITER R. Knowledge in action: logical foundations for specifying and imp lementing dynamical systems[M]. Cambridge: MIT Press, 2001.
[48]BOUTILIER C, REITER R, PRICE B. Symbolic dynamic programming for firstor de r MDPs[C]// Proceedings of the 17th International Joint Conference on Artific ia l Intelligence. Seattle, WA, 2001.
[49]SANNER S, BOUTILIER C. Approximate linear programming for firstorder MDP s[M]. Arlington, Virginia: AUAI Press, 2005.
[50]GUESTRIN C, KOLLER D, PARR R, et al. Efficient solution methods for facto red MDPs[J]. Journal of Artificial Intelligence Research, 2002, 19: 399468. 
[51]KOENIGSBUCH F T, FABIANI P. Symbolic stochastic focused dynamic programmin g with decision diagrams[C]// Proceedings of International P lanning Competition. Cumbria, UK, 2006.
[52]BAHAR R I,FROHM E A, GAONA C M,et al. Algebraic decision diagrams and th ei r applications[C]// IEEE /ACM International Conference on CAD. Santa Cl ara, USA, 1993.
[53]HOEY J, STAUBIN R, HU A, et al. Optimal and approximate stochastic plann in g using decision diagrams[R]. TR200005, University of Bri ti sh Columbia, 2000.
[54]SOMENZI F. Cudd: cu decision diagram package release[Z]. University of Colorado at Boulder, 1998.
[55]DEAN T, KANAZAWA K. A model for reasoning about persistence and causation [J]. Computational Intelligence, 1989, 5(3): 142150.
[56]YOON S W, FERN A, GIVAN R. Learning reactive policies for probabilistic p l anning domains[C]// Proceedings of International Planning Competition. W hi stler, CA, 200 4.
[57]BERTSEKAS D P, TSITSIKLIS J N. Neurodynamic programming[M]. Belmont: A thena Scientific, 1996.
[58]FERN A, YOON S, GIVAN R. Approximate policy iteration with a policy langu ag e bias[C]// Proceedings of the Neural Information Processing Conference. Ista nbulm, Turkey, 2003
[59]FERN A, YOON S, GIVAN R. Learning domainspecific control knowledge from ra ndom walks[C]// Proceedings of 14th International Conference on Automated Plan ning a nd Scheduling. Whistler, British Columbia, Canada, 2004.
[60]MAJERCIK S M, LITTMAN M L. Contingent planning under uncertainty via stoc hastic satisfiability[J]. Artificial Intelligence, 2003, 147(1): 119162.
[61]DAVIS M, LOGEMANN G, LOVELAND D. A machine program for theorem proving[J ]. Comm ACM 5, 1962,5(7):394397.
[62]GIUNCHIGLIA E, KARTHA G N, LIFSCHITZ V. Representing action: indeterminac y and ramifications[J]. Artificial Intelligence, 1997, 95(2): 409438.
[63]YOUNES H S, LITTMAN M L, WEISSMAN D, et al. The first probabilistic track of the international planning competition[J]. Journal of Artificial Intelligen ce Research, 2005,24:851887.
[64]BONET B, GIVAN B. Results of probabilistic track in the 5 th international planning competition[C]// Proceedings of International Planing Competition. Cumbria, UK, 2006.
相似文献/References:
[1]宋泾舸,查建中,陆一平.智能规划研究综述——一个面向应用的视角[J].智能系统学报,2007,2(2):18.
 SONG Jing-ge,CHA Jian-zhong,LU Yi-ping.Survey on AI planning research— an applicationoriented perspective[J].CAAI Transactions on Intelligent Systems,2007,2():18.

备注/Memo

收稿日期:2007-07-19.
基金项目:
国家自然科学基金资助项目(60573067, 60473042);
东北师范大学青年自然科学基金资助项目(20070601)
作者简介:
闫书亚, 女, 1982年生, 硕士研究生, 主要研究方向为智能规划和规划识别.
殷明浩, 男, 1979年生, 助教, 博士研究生, 主要研究方向为自动推理和智能规划.
谷文祥, 男, 1947年生, 教授, 博士生导师, 主要研究方向为智能规划和规划识别、形式语言与自动机理论、模糊数学及其应用, 发表论文百余篇.
通讯作者:闫书亚.E-mail:yansy276@nenu.edu.cn.

更新日期/Last Update: 2009-05-09
Copyright © 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134 邮箱:tis@vip.sina.com