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

Published online by Cambridge University Press:  18 April 2018

Mauro Vallati ,
Lukáš Chrpa  and
Thomas L. Mccluskey
Mauro Vallati
Affiliation:
School of Computing & Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK e-mail: m.vallati@hud.ac.uk
Lukáš Chrpa
Affiliation:
Artificial Intelligence Center, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27, Prague 6, Czech Republic e-mail: chrpaluk@fel.cvut.cz Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, 121 16, Prague, Czech Republic
Thomas L. Mccluskey
Affiliation:
School of Computing & Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK e-mail: tl.mccluskey@hud.ac.uk
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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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Type
Research Article
Information
The Knowledge Engineering Review , Volume 33 , 2018 , e3
Copyright
© Cambridge University Press, 2018 

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References

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