The 2023 International Planning Competition

Ayal Taitler*, Ron Alford, Joan Espasa, Gregor Behnke, Daniel Fišer, Michael Gimelfarb, Florian Pommerening, Scott Sanner, Enrico Scala, Dominik Schreiber, Javier Segovia-Aguas, Jendrik Seipp

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
JournalAI Magazine
Volume45
Issue number2
Pages (from-to)280-296
Number of pages17
ISSN0738-4602
DOIs
Publication statusPublished - 1 Jun 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 The Authors. AI Magazine published by John Wiley & Sons Ltd on behalf of Association for the Advancement of Artificial Intelligence.

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