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The 2023 International Planning Competition

Taitler, Ayal ; Alford, Ron; Espasa, Joan; Behnke, Gregor; Fišer, Daniel; Gimelfarb, Michael; Pommerening, Florian; Sanner, Scott; Scala, Enrico; Schreiber, Dominik ORCID iD icon 1; Segovia-Aguas, Javier; Seipp, Jendrik
1 Institut für Theoretische Informatik (ITI), Karlsruher Institut für Technologie (KIT)

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.


Verlagsausgabe §
DOI: 10.5445/IR/1000170443
Veröffentlicht am 10.05.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 0738-4602, 2371-9621
KITopen-ID: 1000170443
Erschienen in AI Magazine
Verlag Association for the Advancement of Artificial Intelligence (AAAI)
Vorab online veröffentlicht am 05.04.2024
Nachgewiesen in Dimensions
Web of Science
Scopus
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