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: 6471
Citations: Bibtex RefMan EndNote GB/T7714
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,in press.https://doi.org/10.1631/FITEE.1800514 @article{title="Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems", %0 Journal Article TY - JOUR
机器人任务规划:基于PDDL和ASP的任务规划系统实验比较研究关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[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. 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 |
Open peer comments: Debate/Discuss/Question/Opinion
<1>