Scholar4AI Updated 2026-11-02 12:20 UTC+09:00

AAMAS: International Joint Conference on Autonomous Agents and Multiagent Systems (previously the International Conference on Multiagent Systems, ICMAS, changed in 2000) — papers and institutions, 2024–2026

Papers of AAMAS, International Joint Conference on Autonomous Agents and Multiagent Systems (previously the International Conference on Multiagent Systems, ICMAS, changed in 2000) (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. Also as Markdown.

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Key findings#

  • AAMAS (International Joint Conference on Autonomous Agents and Multiagent Systems (previously the International Conference on Multiagent Systems, ICMAS, changed in 2000)) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,550 of its papers from 2024–2026 (434 in 2024, 464 in 2025, 652 in 2026).
  • By the countries of the authors' institutions, the most papers come from the United States (415), China (273) and the United Kingdom (252).
  • The institutions with the most papers are Centre National de la Recherche Scientifique (43), Chinese Academy of Sciences (43) and University of Oxford (40).
  • Its most-cited paper of these years is “LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordination” (2024), with 20 citations.

Papers per year#

AAMAS papers per publication year in OpenAlex (–: not in OpenAlex yet)
YearPapersWith affiliations
2024434431
2025464456
2026652626

Sources#

Where OpenAlex lists the papers
OpenAlex sourceWhich papers
S4306417597The conference's proceedings

Who publishes there#

Institutions with the most AAMAS papers, 2024–2026
#InstitutionPapers
1Centre National de la Recherche Scientifique43
2Chinese Academy of Sciences43
3University of Oxford40
4Institute of Automation38
5University of Chinese Academy of Sciences36
6Tsinghua University35
7King's College London34
8Shanghai Jiao Tong University30
9Carnegie Mellon University30
10Nanyang Technological University28
Countries with the most AAMAS papers, 2024–2026
#CountryPapers
1United States415
2China273
3United Kingdom252
4Germany99
5France89
6Netherlands87
7Italy82
8India80
9Australia70
10Canada65
Keywords with the most AAMAS papers, 2024–2026
#KeywordPapers
1multi-agent systems174
2multi-agent reinforcement learning149
3large language models123
4computational complexity74
5fair division58
6reinforcement learning50
7approximation algorithms47
8LLM agents41
9agent-based modeling39
10mechanism design39

Most-cited papers#

Most-cited AAMAS papers of 2024–2026 (citations counted by OpenAlex)
#CitationsTitlePublishedAuthors
120LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordination2024-05-06Jijia Liu, Chao Yu, Jiaxuan Gao et al.
213Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense2025-05-28Aditya Vikram Singh, Ethan Rathbun, Emma L. Graham et al.
312Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing2024-05-06Tesfay Zemuy Gebrekidan, Sebastian Stein, Timothy J. Norman
411Developing a Multi-agent and Self-adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management2024-05-06Zhenglong Li, Vincent W. L. Tam, Kwan Lawrence Yeung
510On the Limits of Agency in Agent-based Models2025-05-28Ayush Chopra, Shashank Kumar, Nurullah Giray Kuru et al.
69Whom to Trust? Elective Learning for Distributed Gaussian Process Regression2024-05-06Zewen Yang, Xiaobing Dai, Akshat Dubey et al.
79BDI Agents in Natural Language Environments2024-05-06Alexandre Yukio Ichida, Felipe Meneguzzi, Rafael Cauê Cardoso
89Causal Explanations for Sequential Decision-Making in Multi-Agent Systems2024-05-06Bálint Gyevnár, Cheng Wang, Christopher G. Lucas et al.
99Engineering LaCAM*: Towards Real-time, Large-scale, and Near-optimal Multi-agent Pathfinding2024-05-06Keisuke Okumura
108From Explicit Communication to Tacit Cooperation: A Novel Paradigm for Cooperative MARL2024-05-06Dapeng Li, Zhiwei Xu, Bin Zhang et al.

Conferences in the same field#

AAAI · EMNLP · ACL · ICRA · IJCAI · HRI · EC · KR · ICAPS · IROS · Interspeech · NAACL · CIKM · ECAI · EACL · GECCO · IJCAR · CP · SAT · CADE · FOGA

Cite this page

Scholar4AI. (2026). AAMAS: International Joint Conference on Autonomous Agents and Multiagent Systems (previously the International Conference on Multiagent Systems, ICMAS, changed in 2000) — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/aamas

@misc{scholar4ai-proceedings-aamas,
  author = {{Scholar4AI}},
  title = {AAMAS: International Joint Conference on Autonomous Agents and Multiagent Systems (previously the International Conference on Multiagent Systems, ICMAS, changed in 2000) — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/aamas}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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