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An Experimental Study of Influence of Modeling and Solving Techniques on Performance of a Tabled Logic Programming Planner

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Warianty tytułu
Konferencja
Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (22; 22.09.2015; Ferrara; Italy)
Języki publikacji
EN
Abstrakty
EN
Logic programming provides a declarative framework for modeling and solving many combinatorial problems. Until recently, it was not competitive with state-of-the-art planning techniques partly due to search capabilities limited to backtracking. Recent development brought more advanced search techniques to logic programming such as tabling that simplifies implementation and exploitation of more sophisticated search algorithms. Together with rich modeling capabilities this progress brings tabled logic programing on a par with current best planners. This paper describes the planner module of the tabled logic programming language Picat, its modeling capabilities, and core search procedures behind the planner. The major contribution is an experimental comparison of the influence of various modeling techniques, namely factored vs. structured representations of states, control knowledge, and heuristics on the performance of two search procedures – iterative deepening and branch and bound-behind the planner. The paper also compares the Picat planner with winning automated planners both domain dependent and domain independent to demonstrate that the presented techniques are competitive with state-of-the-art.
Słowa kluczowe
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Strony
35--60
Opis fizyczny
Bibliogr. 36 poz., tab., wykr.
Twórcy
autor
  • Charles University, Faculty of Mathematics and Physics, Malostranské náměstí 25, 118 00 Praha 1, Czech Republic
  • Charles University, Faculty of Mathematics and Physics, Malostranské náměstí 25, 118 00 Praha 1, Czech Republic
Bibliografia
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  • [4] Roman Barták and Neng-Fa Zhou. Using tabled logic programming to solve the Petrobras planning problem. Theory and Practice of Logic Programming, 2014;14(4-5):697–710. Available from: https://doi.org/10.1017/S1471068414000295.
  • [5] Roman Barták, Agostino Dovier, Neng-Fa Zhou. On modeling planning problems in tabled logic programming. in: Proceedings of the 17th International Symposium on Principles and Practice of Declarative Programming – PPDP’15, 2015 p. 32–42. doi:10.1145/2790449.2790521.
  • [6] Agostino Dovier, Andrea Formisano, and Enrico Pontelli. Multi-valued Action Languages with Constraints in CLP(FD), Theory and Practice of Logic Programming, 2010;10(2):167–235. doi:10.1017/S1471068410000013.
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  • [11] Patrik Haslum and Ulrich Scholz. Domain knowledge in planning: Representation and use. in: ICAPS Workshop on PDDL. Proceedings, 2003.
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  • [13] International Planning Competitions web site, http://ipc.icaps-conference.org/, Accessed April 5, 2015.
  • [14] Henry Kautz and Bart Selman. Planning as satisfiability. in: Proceedings of ECAI, 1992 p. 359–363. ISBN:0-471-93608-1.
  • [15] Richard E. Korf. Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence, 1985;27(1):97–109. doi:10.1016/0004-3702(85)90084-0.
  • [16] Jonas Kvarnström and Martin Magnusson. Talplanner in the third international planning competition: Extensions and control rules. J. Artificial Intelligence Research (JAIR), 2003;20(1):343–377. Available from: http://dl.acm.org/citation.cfm?id=1622452.1622464.
  • [17] Ailsa H. Land and Alison G. Doig. An automatic method of solving discrete programming problems. Econometrica 1960;28(3):497–520. Available from: http://jmvidal.cse.sc.edu/library/land60a.pdf.
  • [18] Derek Long and Maria Fox. The 3rd International Planning Competition: Results and Analysis, Journal of Artificial Intelligence Research 2003;20(1):1–59. Available from: http://dl.acm.org/citation.cfm?id=1622452.1622453.
  • [19] Drew McDermott. The planning domain definition language manual. CVC Report 98-003, Yale Computer Science Report 1165, 1998.
  • [20] Dana S. Nau, Tsz-Chiu Au, Okhtay Ilghami, Ugur Kuter, J. William Murdock, Dan Wu, and Fusun Yaman. SHOP2: an HTN planning system. J. Artificial Intelligence Research (JAIR), 2003;20(1):379–404. Available from: http://dl.acm.org/citation.cfm?id=1622452.1622465.
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  • [22] Picat web site, http://picat-lang.org/, Accessed July 3, 2015.
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  • [24] Pat Riddle, Mike Barley, Santiago Franco, and Jordan Douglas. Automated Transformation of Problem Representations. in: Proceedings of the Eighth International Symposium on Combinatorial Search (SoCS-2015). AAAI Press, 2015 p. 214–215.
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  • [27] TLPlan web site, http://www.cs.toronto.edu/tlplan/, Accessed April 5, 2015.
  • [28] Alvaro Torralba, Vidal Alcazar, and Daniel Borrajo. SymBA*: A symbolic bidirectional a planner. in: The 2014 International Planning Competition, 2014 p. 105–109.
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  • [30] Neng-Fa Zhou and Christian Theil Have. Efficient tabling of structured data with enhanced hash-consing. Theory and Practice of Logic Programming, 2012;12(4-5):547–563. doi:10.1017/S1471068412000178.
  • [31] Neng-Fa Zhou and Agostino Dovier. A tabled Prolog program for solving Sokoban. Fundamenta Informaticae, 2013;124(4):561–575. doi:10.3233/FI-2013-849.
  • [32] Neng-Fa Zhou, T. Sato, and Y.-D. Shen. Linear tabling strategies and optimizations. Theory and Practice of Logic Programming, 2008;8(1):81–109. Available from: https://doi.org/10.1017/S147106840700316X.
  • [33] Neng-Fa Zhou, Y. Kameya, and T. Sato. Mode-directed tabling for dynamic programming, machine learning, and constraint solving. in: Proceedings of 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2010 p. 213–218. doi:10.1109/ICTAI.2010.103.
  • [34] Neng-Fa Zhou. Combinatorial Search With Picat. 2014. Available from: http://arxiv.org/abs/1405.2538.
  • [35] Neng-Fa Zhou, Roman Barták, Agostino Dovier. Planning as Tabled Logic Programming. Theory and Practice of Logic Programming, 2015;15(4-5):543–558. Available from: https://doi.org/10.1017/S1471068415000216.
  • [36] Neng-Fa Zhou, Håkan Kjellerstrand, and Jonathan Fruhman. Constraint Solving and Planning with Picat. Springer, 2015. ISBN:978-3-319-25881-2.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-528e7475-648e-4be3-81cd-70d01d5bfd0d
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