A Survey of Temporal Planning

Rao Dong-ning; Yang Jin-peng; Liu Yue-chang

doi:10.12052/gdutxb.200127

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

A Survey of Temporal Planning

更新时间：2026-05-27

- A Survey of Temporal Planning
- Vol. 38, Issue 3, Pages: 9-16(2021)
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 嘉应学院计算机学院,广东,梅州,514015
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.200127
  CLC： TP182
- Online First：10 May 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
饶东宁, 杨锦鹏, 刘越畅. 时态规划综述及研究现状[J]. 广东工业大学学报, 2021, 38(3): 9-16.

Rao Dong-ning, Yang Jin-peng, Liu Yue-chang. A Survey of Temporal Planning[J]. 2021, 38(3): 9-16.
饶东宁, 杨锦鹏, 刘越畅. 时态规划综述及研究现状[J]. 广东工业大学学报, 2021, 38(3): 9-16. DOI： 10.12052/gdutxb.200127.

Rao Dong-ning, Yang Jin-peng, Liu Yue-chang. A Survey of Temporal Planning[J]. 2021, 38(3): 9-16. DOI： 10.12052/gdutxb.200127.

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。

Abstract

Automated planning is an important branch of artificial intelligence

which can be widely used in robot

industrial production

and commercial fields. A review is conducted on the temporal planning

which is a frontier and sub-area of automated planning

from three aspects: 1) temporal feature; 2) planning method; 3) applications. The conclusions are: 1) comparing with planning ability

the community has proposed sufficient temporal features; 2) for the time being

state space forward searching based on heuristics is our best choice; 3) researchers are still looking for more and better applications. This survey aims to provide basic concepts for students who are interested in temporal planning.

关键词

Keywords

references

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.

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.

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.

DO M, KAMBHAMPATI S. SAPA: a multi-objective metric temporal planner [J]. Journal of Artificial Intelligence Research, 2003, 20(20): 155-194.

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.

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.

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.

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.

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.

GEREVINI A, LONG D. Plan constraints and Preferences in PDDL3: the language of the fifth international planning[R]. Italy: University of Brescia, 2005.

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.

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.

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.

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.

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.

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.

RANKOOH M F, GHASSEM-SANI G. ITSAT: an efficient SAT-based temporal planner [J]. Journal of Artificial Intelligence Research, 2015, 53: 541-632.

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.

LIU Y C. Expressive temporal planning algorithm under dynamic constraint satisfaction framework [J]. Computer Science, 2012, 39(6): 226-230.

GIGANTE N. Timeline-based planning: expressiveness and complexity[D]. Udine: University of Udine, 2019.

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.

TOYER S, TREVIZAN F, THIEBAUX, SYLVIE, et al . ASNets: deep learning for generalised planning [J ] . Journal of Artificial Intelligence Research, 2020, 68: 1-68.

COOPER M C, MARIS F, REGNIER P. Monotone temporal planning: tractability, extension and applications [J]. Journal of Artificial Intelligence Research, 2014, 50: 447-485.

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.

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.

冯宇轩. 时间相关的分层任务网络规划[D]. 长春: 吉林大学, 2016.

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.

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.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Realization of Intrinsic Safety in Production Process Based on Artificial Intelligence

Emotional Intelligence and Computational Psychophysiology

Research on the Intelligent Science Theory of Oriental Thinking Based on Factor Driven

Modeling of 3D NAND Memory Cells with Taper Angle and Programming Strategy Optimization

Research and Implementation of a Pulsed Laser Ranging Method Based on Digital Mixing

Related Author

Xu Pingping

Huang Guoheng

Zhao Qin

Chen Yijia

Cui Tie-jun

Li Sha-sha

Hu Bin

Zhou Ying-hui

Related Institution

School of Computer Science of Technology, Guangdong University of Technology

Faculty of Applied Sciences, Macao Polytechnic University, Macao

College of Safety Science and Engineering, Liaoning Technical University

School of Business Administration, Liaoning Technical University

School of Information Science and Engineering, Lanzhou University

AI问答

⁰