The 2023 International Planning Competition-Reference-Cited by-同舟云学术

The 2023 International Planning Competition

Published:2024-04-05 Issue:2 Volume:45 Page:280-296
ISSN:0738-4602
Container-title:AI Magazine
language:en
Short-container-title:AI Magazine

Author:

Taitler Ayal¹^ORCID,Alford Ron²^ORCID,Espasa Joan³^ORCID,Behnke Gregor⁴,Fišer Daniel⁵,Gimelfarb Michael¹,Pommerening Florian⁶,Sanner Scott¹,Scala Enrico⁷,Schreiber Dominik⁸,Segovia‐Aguas Javier⁹,Seipp Jendrik¹⁰

Affiliation:

1. University of Toronto Toronto Ontario Canada

2. The MITRE Corporation McLean Virginia USA

3. University of St Andrews St Andrews UK

4. University of Amsterdam Amsterdam The Netherlands

5. Saarland University Saarbrucken Saarland Germany

6. University of Basel Basel Switzerland

7. University of Brescia Brescia Italy

8. Karlsruhe Institute of Technology Karlsruhe Baden‐Württemberg Germany

9. Universitat Pompeu Fabra Barcelona Spain

10. Linköping University Linkoping Östergötland Sweden

Abstract

AbstractIn 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.

Funder

Vetenskapsrådet

University Of Edinburgh

Engineering and Physical Sciences Research Council

Natural Sciences and Engineering Research Council of Canada

European Research Council

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aaai.12169

Reference80 articles.

1. Alford R. G.Behnke D.Höller P.Bercher S.Biundo andD. W.Aha.2016. “Bound to Plan: Exploiting Classical Heuristics via Automatic Translations of Tail‐Recursive HTN Problems.” InProceedings of the 26th International Conference on Automated Planning and Scheduling (ICAPS 2016) 20–28.AAAI Press.

2. Alford R. P.Bercher andD. W.Aha.2015. “Tight Bounds for HTN Planning.” InProceedings of the 25th International Conference on Automated Planning and Scheduling (ICAPS 2015) 7–15.AAAI Press.

3. Behnke G.2021. “Block Compression and Invariant Pruning for SAT‐Based Totally‐Ordered HTN Planning.” InProceedings of the International Conference on Automated Planning and Scheduling volume31 25–35.

4. Behnke G. D.Höller andP.Bercher eds.2021.Proceedings of the 10th International Planning Competition: Planner and Domain Abstracts – Hierarchical Task Network (HTN) Planning Track (IPC 2020).https://ipc2020.hierarchical-task.net/publications/IPC2020Booklet.pdf.

5. Behnke G. D.Höller A.Schmid P.Bercher andS.Biundo.2020. “On Succinct Groundings of HTN Planning Problems.” InProceedings of the AAAI Conference on Artificial Intelligence volume34 9775–9784.