The 2023 international planning competition

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• dc.contributor.author Taitler, Ayal
• dc.contributor.author Alford, Ron
• dc.contributor.author Espasa, Joan
• dc.contributor.author Behnke, Gregor
• dc.contributor.author Fišer, Daniel
• dc.contributor.author Gimelfarb, Michael
• dc.contributor.author Pommerening, Florian
• dc.contributor.author Sanner, Scott
• dc.contributor.author Scala, Enrico
• dc.contributor.author Schreiber, Dominik
• dc.contributor.author Segovia-Aguas, Javier
• dc.contributor.author Seipp, Jendrik
• dc.date.accessioned 2025-05-14T06:08:37Z
• dc.date.available 2025-05-14T06:08:37Z
• dc.date.issued 2024
• dc.description.abstract In this article, we present an overview of the 2023 International Planning Competition. It featured five distinct tracks designed to assess cutting-edge methods and explore the frontiers of planning within these settings: the classical (deterministic) track, the numeric track, the Hierarchical Task Networks (HTN) track, the learning track, and the probabilistic and reinforcement learning track. Each of these tracks evaluated planning methodologies through one or more subtracks, with the goal of pushing the boundaries of current planner performance. To achieve this objective, the competition introduced a combination of well-established challenges and entirely novel ones. Within this article, each track offers an exploration of its historical context, justifies its relevance within the planning landscape, discusses emerging domains and trends, elucidates the evaluation methodology, and ultimately presents the results.
• dc.description.sponsorship Some of the computations for the Learning track were enabled by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through Grant Agreement No. 2022-06725. The numeric track computations used the Cirrus UK National Tier-2 HPC Service at EPCC (http://www.cirrus.ac.uk) funded by the University Of Edinburgh and EPSRC (EP/P020267/1). The HTN track has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 882500). This work was partially funded as part of the MITRE Independent Research and Development Program. Portions of this technical data were produced for the U. S. Government under Contract No. FA8702-19-C-0001 and W56KGU-18-D-0004, and is subject to the Rights in Technical Data-Noncommercial Items Clause DFARS 252.227-7013 (FEB 2014). The Probabilistic and Reinforcement Learning Track has received funding form the Natural Sciences and Engineering Research Council of Canada (NSERC).
• dc.format.mimetype application/pdf
• dc.identifier.citation Taitler A, Alford R, Espasa J, Behnke G, Fišer D, Gimelfarb M, et al. The 2023 international planning competition. AI Mag. 2024 Summer;45(2):280-96. DOI: 10.1002/aaai.12169
• dc.identifier.doi http://dx.doi.org/10.1002/aaai.12169
• dc.identifier.issn 0738-4602
• dc.identifier.uri http://hdl.handle.net/10230/70389
• dc.language.iso eng
• dc.publisher Wiley
• dc.relation.ispartof AI Magazine. 2024 Summer;45(2):280-96
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/882500
• dc.rights © 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.other Planificació -- Concursos
• dc.title The 2023 international planning competition
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion