Taitler, AyalAlford, RonEspasa, JoanBehnke, GregorFišer, DanielGimelfarb, MichaelPommerening, FlorianSanner, ScottScala, EnricoSchreiber, DominikSegovia-Aguas, JavierSeipp, Jendrik2025-05-142025-05-142024Taitler 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.121690738-4602http://hdl.handle.net/10230/70389In 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.application/pdfeng© 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.Planificació -- Concursosinfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1002/aaai.12169info:eu-repo/semantics/openAccess