广东工业大学学报 ›› 2021, Vol. 38 ›› Issue (03): 9-16.doi: 10.12052/gdutxb.200127
饶东宁1, 杨锦鹏1, 刘越畅2
Rao Dong-ning1, Yang Jin-peng1, Liu Yue-chang2
摘要: 作为人工智能领域的一个重要分支, 智能规划被广泛应用于机器人、工业生产、商业应用等领域。时态规划是智能规划的前沿子领域。本文从时态特征、规划方法、应用等三个角度出发, 对时态规划进行综述。与规划能力相比, 时态特征的发展已足够成熟; 基于启发式的状态空间搜索是目前的最佳选择; 研究人员仍在寻找更多更好的应用场景。本文旨在用通俗易懂的方式帮助入门学者快速认识时态规划。
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[1] FIKE R E, NILSSON J N. STRIPS: a new approach to the application of theorem proving to problem solving [J]. Artificial Intelligence, 1971, 2(3-4): 189-208. [2] PEDNAULT E. ADL: exploring the middle ground between STRIPS and the situation calculus[C]//Proceeding of the First International Conference on Principles of Knowledge Representation and Reasoning. San Francisco: Morgan Kaufmann Publishers Inc, 1989: 324-332. [3] SMITH D, WELD D S. Temporal planning with Mutual exclusion reasoning[C]//Proceeding of the Sixteenth International Joint Conference on Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers Inc, 1999: 326-337. [4] DO M, KAMBHAMPATI S. SAPA: a multi-objective metric temporal planner [J]. Journal of Artificial Intelligence Research, 2003, 20(20): 155-194. [5] FOX M, LONG D. Fast temporal planning in a Graph plan framework. [C]//Proceedings of AIPS-02 Workshop on Planning for Temporal Domains. Palo Alto, CA: AAAI Press, 2002: 9-17. [6] SMITH D E, FRANK J, JONSSON A, et al. Bridging the gap between planning and scheduling [J]. Knowledge Engineering Review, 2000, 15(1): 47-83. [7] FOX M, LONG D. PDDL2.1: an extension to PDDL for expressing temporal planning domains [J]. Journal of Artificial Intelligence Research, 2003, 20: 61-124. [8] EDELKAMP S, HOFFMANN J. PDDL2.2: The language for the classical part of the fourth international planning competition[R]. Palo Alto, CA: AAAI Press, 2004. [9] 饶东宁, 蒋志华, 姜云飞. 规划领域定义语言的演进综述[J]. 计算机工程与应用, 2010, 46(22): 23-25. RAO D N, JIANG Z H, JIANG Y F. Review on evolution of planning domains definition language [J]. Computer Engineering and Applications, 2010, 46(22): 23-25. [10] GEREVINI A, LONG D. Plan constraints and Preferences in PDDL3: the language of the fifth international planning[R]. Italy: University of Brescia, 2005. [11] FOX M, LONG D. PDDL+level5: an extension to PDDL2.1 for modeling planning domains with Continuous time-dependent effects[C]//Proceedings of the Third International NASA Workshop on Planning and Scheduling for Space. Palo Alto, CA: AAAI Press, 2003: 1-48. [12] EYERICH P, MATTMULLER R, ROGER G. Using the context enhanced additive heuristic for temporal and numeric planning[C]//Proceedings of the Nineteenth International Conference on Automated Planning and Scheduling. Palo Alto, CA: AAAI Press, 2009: 130-137. [13] VIDAL V. The YAHSP planning system: forward heuristic search with lookahead plans analysis[C]//Proceedings of the Fourth International Planning Competition. Palo Alto, CA: AAAI Press, 2004: 56-58. [14] SCHWARTZ P, POLLACK M E. Planning with Disjunctive temporal constraints[C]//Proceedings of ICAPS-'04 Workshop on Integrating Planning into Scheduling. PaloAlto, CA: AAAI Press, 2004: 67-74. [15] SAPENO O, MARZAL E, ONAINDIA E. TFLAP: a Temporal forward partial-order planner[EB/OL]. (2018-06-29)[2020-11-17]. https://ipc2018.bitbucket.io/#temporal [16] 柴啸龙, 姜云飞, 陈蔼祥. 基于规划图的蚁群规划算法[J]. 计算机研究与发展, 2009, 46(9): 1471-1479. CHAI X L, JIANG Y F, CHEN A X. Ant colony planning algorithm based on planning graph [J]. Journal of Computer Research and Development, 2009, 46(9): 1471-1479. [17] 伍丽华, 姜云飞, 陈蔼祥. 规划图框架下用遗传算法求解时态规划问题[J]. 计算机研究与发展, 2008, 45(6): 981-990. WU L H, JIANG Y F, CHEN A X. Using genetic algorithm to solve temporal planning problems under the framework of the planning graph [J]. Journal of Computer Research and Development, 2008, 45(6): 981-990. [18] FURELOS-BALANCO D, JONSSON A. CP4TP: a Classical planning for temporal planning portfolio [EB/OL]. (2018-06-29)[2020-11-17]. https://ipc2018.bit-bucket.io/#temporal [19] RANKOOH M F, GHASSEM-SANI G. ITSAT: an efficient SAT-based temporal planner [J]. Journal of Artificial Intelligence Research, 2015, 53: 541-632. [20] 伍丽华, 姜云飞, 陈蔼祥, 等. 时态规划中基于CSP技术的时态约束方法[J]. 计算机学报, 2012, 35(8): 1759-1766. WU L H, JIANG Y F, CHEN A X, et al. ACSP-based approach for temporal constraints in temporal planning [J]. Chinese Journal of Computer, 2012, 35(8): 1759-1766. [21] 刘越畅. 基于动态约束满足框架的强表达时态规划算法[J]. 计算机科学, 2012, 39(6): 226-230. LIU Y C. Expressive temporal planning algorithm under dynamic constraint satisfaction framework [J]. Computer Science, 2012, 39(6): 226-230. [22] GIGANTE N. Timeline-based planning: expressiveness and complexity[D]. Udine: University of Udine, 2019. [23] 白丽赟, 胡学敏, 宋昇, 等. 基于深度级联神经网络的自动驾驶运动规划模型[J]. 计算机应用, 2019, 39(10): 2870-2875. BAI L Y, HU X M, SONG S, et al. Motion planning model based on deep cascaded neural network for autonomous driving [J]. Journal of Computer Applications, 2019, 39(10): 2870-2875. [24] TOYER S, TREVIZAN F, THIEBAUX, SYLVIE, et al. ASNets: deep learning for generalised planning [J]. Journal of Artificial Intelligence Research, 2020, 68: 1-68. [25] COOPER M C, MARIS F, REGNIER P. Monotone temporal planning: tractability, extension and applications [J]. Journal of Artificial Intelligence Research, 2014, 50: 447-485. [26] SMITH S J J, NAU D S, THROOP T A. Computer bridge: a big win for AI planning [J]. AI Magazine, 1998, 19(2): 93-106. [27] FERNANDEZ-OLIVARES J, PEREZ R. Driver activity recognition by means of temporal HTN planning[C]//Proceedings of the International Conference on Automated Planning and Scheduling. Palo Alto, CA: AAAI Press, 2020: 375-383. [28] 冯宇轩. 时间相关的分层任务网络规划[D]. 长春: 吉林大学, 2016. [29] CARRENO Y, PAIRET È, PETILLOT Y, et al. A decentralised strategy for heterogeneous AUV missions via goal distribution and temporal planning[C]//Proceedings of the International Conference on Automated Planning and Scheduling. Palo Alto, CA: AAAI Press, 2020: 431-439. [30] KIAM J J, SCALA E, JAVEGA M R, et al. An AI-based planning framework for HAPS in a time-varying environment[C]//Proceedings of the International Conference on Automated Planning and Scheduling. Palo Alto, CA: AAAI Press, 2020: 412-420. |
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