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2018 Volume 33
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RESEARCH ARTICLE   Open Access    

What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

  • 1.

    School of Computing & Engineering

  • 2.

    Artificial Intelligence Center

  • 3.

    Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, 121 16, Prague, Czech Republic

  • 4.

    School of Computing & Engineering

More Information
  • Abstract: The International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.
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  • Alcázar V., Veloso M. M. & Borrajo D. 2011. Adapting a rapidly-exploring random tree for automated planning. In Proceedings of the Fourth Annual Symposium on Combinatorial Search, SOCS.

    Google Scholar

    Baier J. A. & McIlraith S. A. 2008. Planning with preferences. AI Magazine 29(4), 25–36.

    Google Scholar

    Balint A., Belov A., Diepold D., Gerber S., Järvisalo M. & Sinz C. 2012. SAT challenge 2012. http://www.satcompetition.org/

    Google Scholar

    Balint A., Belov A., Heule M. J. & Järvisalo M. 2013. SAT competition 2013. http://www.satcompetition.org/

    Google Scholar

    Belov A., Diepold D., Heule M. J. & Järvisalo M. 2014. SAT competition 2014. http://www.satcompetition.org/

    Google Scholar

    Bylander T. 1996. A probabilistic analysis of prepositional strips planning. Artificial Intelligence 81(1), 241–271.

    Google Scholar

    Calimeri F., Gebser M., Maratea M. & Ricca F. 2016. Design and results of the fifth answer set programming competition. Artificial Intelligence 231, 151–181.

    Google Scholar

    Cenamor I., De La Rosa T. & Fernández F. 2012. Mining IPC-2011 results. In WS-IPC 2012.

    Google Scholar

    Chen Y., Wah B. W. & Hsu C.-W. 2006. Temporal planning using subgoal partitioning and resolution in sgplan. Journal of Artificial Intelligence Research 26, 323–369.

    Google Scholar

    Chrpa L., McCluskey T., Vallati M. & Vaquero T. 2017. The fifth international competition on knowledge engineering for planning and scheduling: summary and trends. AI Magazine 38(1), 104–106.

    Google Scholar

    Domshlak C., Hoffmann J. & Katz M. 2015. Red-black planning: a new systematic approach to partial delete relaxation. Artificial Intelligence 221, 73–114.

    Google Scholar

    Dréo J., Savéant P., Schoenauer M. & Vidal V. 2011. Divide-and-evolve: the marriage of Descartes and Darwin. In Proceedings of the 7th International Planning Competition (IPC).

    Google Scholar

    Edelkamp S., Kissmann P. & Torralba Á. 2015. Bdds strike back (in AI planning). In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.

    Google Scholar

    Fawcett C., Vallati M., Hutter F., Hoffmann J., Hoos H. H. & Leyton-Brown K. 2014. Improved features for runtime prediction of domain-independent planners. In Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling, ICAPS.

    Google Scholar

    Fox M. & Long D. 2003. PDDL2.1: an extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20, 61–124.

    Google Scholar

    Gerevini A., Saetti A. & Serina I. 2003. Planning through stochastic local search and temporal action graphs. Journal of Artificial Intelligence Research 20, 239–290.

    Google Scholar

    Gerevini A., Saetti A. & Vallati M. 2014. Planning through automatic portfolio configuration: the pbp approach. Journal of Artificial Intelligence Research 50, 639–696.

    Google Scholar

    Gerevini A. E., Haslum P., Long D., Saetti A. & Dimopoulos Y. 2009. Deterministic planning in the fifth international planning competition: Pddl3 and experimental evaluation of the planners. Artificial Intelligence 173(5), 619–668.

    Google Scholar

    Gulić M., Olivares R. & Borrajo D. 2016. Using automated planning for traffic signals control. PROMET-Traffic&Transportation 28(4), 383–391.

    Google Scholar

    Haslum P. 2011. Computing genome edit distances using domain-independent planning. In ICAPS'11 Scheduling and Planning Applications Workshop (SPARK).

    Google Scholar

    Helmert M. 2003. Complexity results for standard benchmark domains in planning. Artificial Intelligence 143(2), 219–262.

    Google Scholar

    Helmert M. 2006. The fast downward planning system. Journal of Artificial Intelligence Research 26, 191–246.

    Google Scholar

    Helmert M. & Domshlak C. 2011. LM-cut: optimal planning with the landmark-cut heuristic. In IPC 2011 Planner Abstracts.

    Google Scholar

    Helmert M. & Röger G. 2008. How good is almost perfect? In Proceedings of AAAI, 944–949.

    Google Scholar

    Helmert M., Röger G. & Karpas E. 2011. Fast downward stone soup: a baseline for building planner portfolios. In ICAPS 2011 Workshop on Planning and Learning, 28–35.

    Google Scholar

    Hoffmann J. 2003. The Metric-FF planning system: translating ‘ignoring delete lists’ to numeric state variables. Journal Artificial Intelligence Research 20, 291–341.

    Google Scholar

    Hoffmann J. 2011. Analyzing search topology without running any search: on the connection between causal graphs and h+. Journal of Artificial Intelligence Research 41, 155–229.

    Google Scholar

    Hoffmann J., Edelkamp S., Thiébaux S., Englert R., dos F., Liporace S. & Trüg S. 2006. Engineering benchmarks for planning: the domains used in the deterministic part of IPC-4. Journal of Artificial Intelligence Research 26, 453–541.

    Google Scholar

    Hoffmann J. & Nebel B. 2001. The FF planning system: fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14, 253–302.

    Google Scholar

    Howe A. E. & Dahlman E. 2002. A critical assessment of benchmark comparison in planning. Journal of Artificial Intelligence Research 17(1), 1–33.

    Google Scholar

    Howey R., Long D. & Fox M. 2004. Val: automatic plan validation, continuous effects and mixed initiative planning using PDDL. In Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-04), 294–301. IEEE.

    Google Scholar

    Hurley B. & O'Sullivan B. 2015. Statistical regimes and runtime prediction. In International Joint Conference on Artificial Intelligence (IJCAI). AAAI Press.

    Google Scholar

    Kanefsky B. & Taylor W. 1991. Where the really hard problems are. In Proceedings of IJCAI 91, 163–169.

    Google Scholar

    Lipovetzky N. & Geffner H. 2011. Searching for plans with carefully designed probes. In Proceedings of ICAPS.

    Google Scholar

    Long D. & Fox M. 2003. The 3rd international planning competition: results and analysis. Journal of Artificial Intelligence Research 20, 1–59.

    Google Scholar

    López C. L., Celorrio S. J. & Olaya Á. G. 2015. The deterministic part of the seventh international planning competition. Artificial Intelligence 223, 82–119.

    Google Scholar

    Matloob R. & Soutchanski M. 2016. Exploring organic synthesis with state-of-the-art planning techniques. In Proceedings of ICAPS ’16 Scheduling and Planning Applications Workshop (SPARK).

    Google Scholar

    Nakhost H. & Müller M. 2010. Action elimination and plan neighborhood graph search: two algorithms for plan improvement. In The Twentieth International Conference on Automated Planning and Scheduling (ICAPS), 121–128.

    Google Scholar

    Nissim R., Hoffmann J. & Helmert M. 2011. The merge-and-shrink planner: bisimulation-based abstraction for optimal planning. In IPC 2011 Planner Abstracts, 106–107.

    Google Scholar

    Núñez S., Borrajo D. & López C. L. 2015. Automatic construction of optimal static sequential portfolios for AI planning and beyond. Artificial Intelligence 226, 75–101.

    Google Scholar

    Ramirez M., Lipovetzky N. & Muise C. 2014. Lightweight automated planning toolkit. Technical report, http://lapkt. org.

    Google Scholar

    Richter S. & Westphal M. 2010. The LAMA planner: guiding cost-based anytime planning with landmarks. Journal Artificial Intelligence Research 39, 127–177.

    Google Scholar

    Richter S., Westphal M. & Helmert M. 2011. Lama 2008 and 2011. In International Planning Competition, 117–124.

    Google Scholar

    Rintanen J. 2004. Phase transitions in classical planning: an experimental study. In ICAPS 2004, 101–110.

    Google Scholar

    Rintanen J. 2012. Engineering efficient planners with SAT. In Proceedings of ECAI, 684–689.

    Google Scholar

    Rizzini M., Fawcett C., Vallati M., Gerevini A. E. & Hoos H. 2015. Portfolio methods for optimal planning: an empirical analysis. In Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI-15). IEEE.

    Google Scholar

    Rossi F., Van Beek P. & Walsh T. 2006. Handbook of constraint programming. Elsevier.

    Google Scholar

    Thimm M., Villata S., Cerutti F., Oren N., Strass H. & Vallati M. 2016. Summary report of the first international competition on computational models of argumentation. AI Magazine 37, 102.

    Google Scholar

    Torralba A. & Alcázar V. 2013. Constrained symbolic search: on mutexes, BDD minimization and more. In Sixth Annual Symposium on Combinatorial Search (SoCS).

    Google Scholar

    Torralba Á., Edelkamp S. & Kissmann P. 2013. Transition trees for cost-optimal symbolic planning. In Twenty-Third International Conference on Automated Planning and Scheduling (ICAPS).

    Google Scholar

    Valenzano R. A., Nakhost H., Müller M., Schaeffer J. & Sturtevant N. R. 2012. Arvandherd: parallel planning with a portfolio. In ECAI, 786–791.

    Google Scholar

    Vallati M., Chrpa L., Grzes M., McCluskey T., Roberts M. & Sanner S. 2015a. The 2014 international planning competition: progress and trends. AI Magazine 36, 90–98.

    Google Scholar

    Vallati M., Chrpa L. & Kitchin D. E. 2015b. Portfolio-Based Planning: State of the Art, Common Practice and Open Challenges. AI Communications 28, 717–733.

    Google Scholar

    Vallati M., Chrpa L. & McCluskey T. L. 2014. The 2014 IPC: description of participating planners of the deterministic track. https://helios.hud.ac.uk/scommv/IPC-14/planners_actual.html

    Google Scholar

    Vallati M., Hutter F., Chrpa L. & McCluskey T. L. 2015c. On the effective configuration of planning domain models. In International Joint Conference on Artificial Intelligence (IJCAI). AAAI Press.

    Google Scholar

    Vallati M. & Vaquero T. 2015. Towards a protocol for benchmark selection in IPC. In The 4th Workshop of the International Planning Competition.

    Google Scholar

    Wilcoxon F. & Wilcox R. A. 1964. Some Rapid Approximate Statistical Procedures. American Cyanamid Co.

    Google Scholar

  • Cite this article

    Mauro Vallati, Lukáš Chrpa, Thomas L. Mccluskey. 2018. What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask). The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000012
    Mauro Vallati, Lukáš Chrpa, Thomas L. Mccluskey. 2018. What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask). The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000012
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RESEARCH ARTICLE   Open Access    

What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

  • 1.

    School of Computing & Engineering

  • 2.

    Artificial Intelligence Center

  • 3.

    Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, 121 16, Prague, Czech Republic

  • 4.

    School of Computing & Engineering

  • Published online: 18 April 2018
The Knowledge Engineering Review  33,  2018, 33(1)  |  Cite this article

Abstract: Abstract: The International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.

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    Acknowledgments

    • The research was partly funded by the UK EPSRC Autonomous and Intelligent Systems Programme (grant no. EP/J011991/1). We want to thank all the people that submitted a planner to the deterministic part of the IPC 2014. Also, to all of you that suggested a domain to be included in the tracks, even if some were not accepted: Patrik Haslum submitted the GED domain; Tomàs de la Rosa and Raquel Fuentetaja sent us the Pizza and Childsnack domains; Joerg Hoffmann provided the Crisp domain; Jussi Rintanen is behind the Maintenance domain; Jaanus Piip and Juhan Ernits submitted the Nurse Rostering domain; Héctor Luis Palacios prepared a large number of conformant domains, such as Grid, Cube, Emptyroom and Bomb; Nathan Robinson, Christian Muise and Charles Gretton sent us the Cave Diving domain; William Westerman for the Airport domain and, last but not least, Simon Parkinson provided the domains Calibration and Uncertainity. We do also want to thank Daniel L. Kovacs for making available a couple of manuscripts with a formal specification of PDDL 3.1. We do feel in debt with Ibad Kureshi, John Brennan and, in general, to the High Performance Computing Research Group (HPC) of the University of Huddersfield for their assistance in configuring and making available the DES system and, moreover, for their continuous support during the testing phase. The authors also want to thank Rick Valenzano, for the support provided in understanding the behaviour of ArvandHerd. Very importantly as well, to the IPC council for providing extensive comments and offering a lot of helpful suggestions. Our most sincere thanks to Carlos Linares López and Sergio Jimenez Celorrio for inviting us to their university, their assistance with so much insight and all the material produced at the previous IPC. The authors also thank the Sportsman Pub in Huddersfield, which supported us with good beer and a comfortable place for taking important decisions. Finally, the authors acknowledge the sponsorship of the University of Huddersfield.

    • http://www.icaps-conference.org/index.php/Main/Competitions

    • https://helios.hud.ac.uk/scommv/IPC-14/

    • https://helios.hud.ac.uk/scommv/IPC-14/planners_actual.html

    • inspired by the European Network in Autonomic Road Transport: www.cost-arts.org

    • http://argumentationcompetition.org

    • More details can be found here: http://www.fast-downward.org/IpcPlanners

    • Note that if FDDS-1 was entered into 2014, it would have been involved in benchmark generation. Not being able to generate any solutions to a set of problems would not necessarily force that set to be replaced, however—there were a number of IPC 2014 planners in this situation.

    • http://paradiseo.gforge.inria.fr/

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    Mauro Vallati, Lukáš Chrpa, Thomas L. Mccluskey. 2018. What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask). The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000012
    Mauro Vallati, Lukáš Chrpa, Thomas L. Mccluskey. 2018. What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask). The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000012
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