Full Text:   <2163>

Summary:  <1316>

CLC number: TP242

On-line Access: 2019-04-09

Received: 2018-08-29

Revision Accepted: 2019-02-07

Crosschecked: 2019-03-14

Cited: 0

Clicked: 6588

Citations:  Bibtex RefMan EndNote GB/T7714


Yu-qian Jiang


Shi-qi Zhang


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.3 P.363-373


Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems

Author(s):  Yu-qian Jiang, Shi-qi Zhang, Piyush Khandelwal, Peter Stone

Affiliation(s):  Department of Computer Science, The University of Texas at Austin, TX 78712, USA; more

Corresponding email(s):   jiangyuqian@utexas.edu, szhang@cs.binghamton.edu, piyushk@gmail.com, pstone@cs.utexas.edu

Key Words:  Task planning, Robotics, Planning domain description language (PDDL), Answer set programming (ASP)

Yu-qian Jiang, Shi-qi Zhang, Piyush Khandelwal, Peter Stone. Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(3): 363-373.

@article{title="Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems",
author="Yu-qian Jiang, Shi-qi Zhang, Piyush Khandelwal, Peter Stone",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems
%A Yu-qian Jiang
%A Shi-qi Zhang
%A Piyush Khandelwal
%A Peter Stone
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 3
%P 363-373
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800514

T1 - Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems
A1 - Yu-qian Jiang
A1 - Shi-qi Zhang
A1 - Piyush Khandelwal
A1 - Peter Stone
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 3
SP - 363
EP - 373
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800514

Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solve a variety of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this study, we empirically compare the performance of state-of-the-art planners that use either the planning domain description language (PDDL) or answer set programming (ASP) as the underlying action language. PDDL is designed for task planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used to solve task planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects or tasks in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general-purpose planning systems for particular robot task planning domains.


摘要:面对无法用单一动作解决的复杂任务时,机器人需要通过任务规划算法生成一个动作序列。现有任务规划器可以帮助智能机器人开发人员解决许多种类任务规划问题。然而,不同规划器有不同优势和劣势,没有统一准则针对问题选择规划器。本文比较了目前最先进的基于规划领域定义语言(planning domain description language, PDDL)和回答集程序(answer set programming, ASP)规划器的性能。PDDL是特别为任务规划而设计的动作语言,被广泛应用于各种规划问题。ASP主要用于知识推理,同时也能解决任务规划问题。针对这两种语言,本文使用尽可能相同的领域描述。实验结果表明,基于PDDL的规划器善于解决需要生成较多待执行动作的问题,而基于ASP的规划器更适合解决涉及对象较多的任务,或需对前提条件和后置条件做复杂推理的规划问题。针对具体机器人规划问题,本文得到的结论可以帮助研究人员从通用规划系统中选择合适规划器。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Babb J, Lee J, 2015. Action language BC+: preliminary report. Proc 29th AAAI Conf on Artificial Intelligence, p.1424-1430.

[2]Calimeri F, Gebser M, Maratea M, et al., 2016. Design and results of the fifth answer set programming competition. Artif Intell, 231:151-181.

[3]Cambon S, Alami R, Gravot F, 2009. A hybrid approach to intricate motion, manipulation and task planning. Int J Robot Res, 28(1):104-126.

[4]Chen XP, Ji JM, Jiang JQ, et al., 2010. Developing high-level cognitive functions for service robots. Proc 9th Int Conf on Autonomous Agents and Multiagent Systems, p.989-996.

[5]Coles A, Coles A, Olaya AG, et al., 2012. A survey of the seventh international planning competition. AI Mag, 33(1):83-88.

[6]de la Rosa T, Olaya AG, Borrajo D, 2007. Using cases utility for heuristic planning improvement. Int Conf on Case-Based Reasoning, p.137-148.

[7]Erdem E, Patoglu V, 2018. Applications of ASP in robotics. KI-Künstl Intell, 32(2-3):143-149.

[8]Erdem E, Aker E, Patoglu V, 2012. Answer set programming for collaborative housekeeping robotics: representation, reasoning, and execution. Intell Ser Robot, 5(4):275-291.

[9]Erdem E, Gelfond M, Leone N, 2016. Applications of answer set programming. AI Mag, 37(3):53-58.

[10]Fawcett C, Vallati M, Hutter F, et al., 2014. Improved features for runtime prediction of domain-independent planners. Proc 24th Int Conf on Automated Planning and Scheduling, p.355-359.

[11]Fikes RE, Nilsson NJ, 1971. Strips: a new approach to the application of theorem proving to problem solving. Artif Intell, 2(3-4):189-208.

[12]Gebser M, Grote T, Schaub T, 2010. Coala: a compiler from action languages to ASP. European Workshop on Logics in Artificial Intelligence, p.360-364.

[13]Gebser M, Kaminski R, Knecht M, et al., 2011. plasp: a prototype for PDDL-based planning in ASP. In: Delgrande JP, Faber W (Eds.), Logic Programming and Nonmonotonic Reasoning. Springer, Berlin, p.358-363.

[14]Gebser M, Kaminski R, Kaufmann B, et al., 2014. Clingo=ASP+control: preliminary report. http://arxiv.org/abs/1405.3694

[15]Gelfond M, Kahl Y, 2014. Knowledge Representation, Reasoning, and the Design of Intelligent Agents the Answer-Set Programming Approach. Cambridge University Press, Cambridge.

[16]Gelfond M, Lifschitz V, 1998. Action languages. Electron Trans Artif Intell, 3(6):195-210.

[17]Giunchiglia E, Lee J, Lifschitz V, et al., 2004. Nonmonotonic causal theories. Artif Intell, 153(1-2):49-104.

[18]Helmert M, 2006. The fast downward planning system. J Artif Intell Res, 26:191-246.

[19]Helmert M, Röger G, Karpas E, 2011. Fast downward stone soup: a baseline for building planner portfolios. Int Conf on Automated Planning and Scheduling Workshop on Planning and Learning, p.28-35.

[20]Hoffmann J, 2001. FF: the fast-forward planning system. AI Mag, 22(3):57-62.

[21]Khandelwal P, Zhang SQ, Sinapov J, et al., 2017. BWIBots: a platform for bridging the gap between AI and human–robot interaction research. Int J Robot Res, 36(5-7):635-659.

[22]Lee J, Lifschitz V, Yang F, 2013. Action language BC: preliminary report. Proc 23rd Int Joint Conf on Artificial Intelligence, p.983-989.

[23]Leyton-Brown K, Nudelman E, Shoham Y, 2002. Learning the empirical hardness of optimization problems: the case of combinatorial auctions. Int Conf on Principles and Practice of Constraint Programming, p.556-572.

[24]Lifschitz V, 1997. Two components of an action language. Ann Math Artif Intell, 21(2-4):305-320.

[25]Lifschitz V, 2002. Answer set programming and plan generation. Artif Intell, 138(1-2):39-54.

[26]Lifschitz V, 2008. What is answer set programming? Proc 23rd National Conf on Artificial Intelligence, p.1594-1597.

[27]Lo SY, Zhang S, Stone P, 2018. PETLON: planning efficiently for task-level-optimal navigation. Proc 17th Conf on Autonomous Agents and Multiagent Systems, p.220-228.

[28]McDermott D, 2003. The formal semantics of processes in PDDL. Proc ICAPS Workshop on PDDL, p.101-155.

[29]McDermott D, Ghallab M, Howe A, et al., 1998. PDDL—the planning domain definition language. http://www.cs.yale.edu/homes/dvm/

[30]Miura S, Fukunaga A, 2017. Automatic extraction of axioms for planning. Proc 27th Int Conf on Automated Planning and Scheduling, p.218-227.

[31]Richter S, Westphal M, Helmert M, 2011. Lama 2008 and 2011. Int Planning Competition, p.117-124.

[32]Thiébaux S, Hoffmann J, Nebel B, 2005. In defense of PDDL axioms. Artif Intell, 168(1-2):38-69.

[33]Yang F, Khandelwal P, Leonetti M, et al., 2014. Planning in answer set programming while learning action costs for mobile robots. AAAI Spring Symp on Knowledge Representation and Reasoning in Robotics, p.71-78.

[34]Zhang S, Yang F, Khandelwal P, et al., 2015. Mobile robot planning using action language BC with an abstraction hierarchy. Proc 13th Int Conf on Logic Programming and Nonmonotonic Reasoning, p.502-516.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE