---
title: "ICAPS (A*): papers and universities"
description: "ICAPS (International Conference on Automated Planning and Scheduling) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 224 of its papers from 2024–2026 (82 in 2024, 51 in 2025, 91 in 2026). By the countries of the authors' institutions, the most papers come from the United States (62), Germany (33) and Australia (32)."
url: https://scholar4ai.com/proceedings/icaps
markdown_url: https://scholar4ai.com/proceedings/icaps.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "ICAPS (International Conference on Automated Planning and Scheduling) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 224 of its papers from 2024–2026 (82 in 2024, 51 in 2025, 91 in 2026). (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# ICAPS: International Conference on Automated Planning and Scheduling — papers and institutions, 2024–2026

Papers of ICAPS, International Conference on Automated Planning and Scheduling (CORE rank A*; field: Artificial intelligence), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02.

All conferences: https://scholar4ai.com/proceedings.md

## Key findings

- ICAPS (International Conference on Automated Planning and Scheduling) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 224 of its papers from 2024–2026 (82 in 2024, 51 in 2025, 91 in 2026).
- By the countries of the authors' institutions, the most papers come from the United States (62), Germany (33) and Australia (32).
- The institutions with the most papers are Australian National University (17), University of Basel (17) and Saarland University (16).
- Its most-cited paper of these years is “SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments” (2024), with 67 citations.

## Papers per year

*ICAPS papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 82 | 82 |
| 2025 | 51 | 51 |
| 2026 | 91 | 89 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4387283601](https://openalex.org/S4387283601) | The conference's proceedings |

## Who publishes there

*Institutions with the most ICAPS papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | Australian National University | 17 |
| 2 | University of Basel | 17 |
| 3 | Saarland University | 16 |
| 4 | University of Brescia | 15 |
| 5 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | 12 |
| 6 | Linköping University | 10 |
| 7 | Université de Toulouse | 10 |
| 8 | Czech Technical University in Prague | 10 |
| 9 | Aalborg University | 10 |
| 10 | German Research Centre for Artificial Intelligence | 9 |

*Countries with the most ICAPS papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | United States | 62 |
| 2 | Germany | 33 |
| 3 | Australia | 32 |
| 4 | United Kingdom | 31 |
| 5 | Italy | 23 |
| 6 | Switzerland | 17 |
| 7 | France | 17 |
| 8 | China | 15 |
| 9 | Denmark | 13 |
| 10 | Spain | 13 |

*Keywords with the most ICAPS papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | classical planning | 21 |
| 2 | automated planning | 20 |
| 3 | PDDL | 14 |
| 4 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 13 |
| 5 | multi-agent path finding | 13 |
| 6 | heuristic search | 12 |
| 7 | A* algorithm | 10 |
| 8 | computational complexity | 9 |
| 9 | numeric planning | 9 |
| 10 | scalability | 9 |

## Most-cited papers

*Most-cited ICAPS papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 67 | [SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments](https://doi.org/10.1609/icaps.v34i1.31506) | 2024-05-30 | Abhinav Rajvanshi, Karan Sikka, Xiao Lin et al. |
| 2 | 54 | [On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)](https://doi.org/10.1609/icaps.v34i1.31503) | 2024-05-30 | Vishal Pallagani, Bharath Chandra Muppasani, Kaushik Roy et al. |
| 3 | 23 | [Online Control of Adaptive Large Neighborhood Search Using Deep Reinforcement Learning](https://doi.org/10.1609/icaps.v34i1.31507) | 2024-05-30 | Robbert Reijnen, Yingqian Zhang, Hoong Chuin Lau et al. |
| 4 | 13 | [Large Language Models as Planning Domain Generators](https://doi.org/10.1609/icaps.v34i1.31502) | 2024-05-30 | James T. Oswald, Kavitha Srinivas, Harsha Kokel et al. |
| 5 | 12 | [PDDL+ Models for Deployable yet Effective Traffic Signal Optimisation](https://doi.org/10.1609/icaps.v34i1.31473) | 2024-05-30 | Anas El Kouaiti, Francesco Percassi, Alessandro Saetti et al. |
| 6 | 11 | [DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systems](https://doi.org/10.1609/icaps.v35i1.36130) | 2025-09-16 | Junwei Yu, Yepeng Ding, Hiroyuki Sato |
| 7 | 10 | [Improving Learnt Local MAPF Policies with Heuristic Search](https://doi.org/10.1609/icaps.v34i1.31522) | 2024-05-30 | Rishi Veerapaneni, Qian Wang, Kevin Ren et al. |
| 8 | 8 | [Planning and Execution in Multi-Agent Path Finding: Models and Algorithms](https://doi.org/10.1609/icaps.v34i1.31534) | 2024-05-30 | Yue Zhang, Zhe Chen, Daniel Harabor et al. |
| 9 | 7 | [Non-deterministic Planning for Hyperproperty Verification](https://doi.org/10.1609/icaps.v34i1.31457) | 2024-05-30 | Raven Beutner, Bernd Finkbeiner |
| 10 | 7 | [Decentralized, Decomposition-Based Observation Scheduling for a Large-Scale Satellite Constellation](https://doi.org/10.1609/icaps.v34i1.31535) | 2024-05-30 | Itai Zilberstein, Ananya Rao, Matthew Salis et al. |

## Cite this page

Scholar4AI. (2026). ICAPS: International Conference on Automated Planning and Scheduling — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/icaps

```bibtex
@misc{scholar4ai-proceedings-icaps,
  author = {{Scholar4AI}},
  title = {ICAPS: International Conference on Automated Planning and Scheduling — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/icaps}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).