AI Agent

Peer-to-Peer Energy Trading in Dairy Farms using Multi-Agent Reinforcement Learning

基于多智能体强化学习的奶牛场点对点能源交易

Mian Ibad Ali Shah, Marcos Eduardo Cruz Victorio, Maeve Duffy, Enda Barrett, Karl Mason

机构 * School of Computer Science, College of Science and Engineering, University of Galway（Galway大学计算机科学学院） ； School of Engineering, College of Science and Engineering, University of Galway（Galway大学工程学院） 纠错

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

AI总结 本研究利用多智能体强化学习和点对点能源交易，降低奶牛场电力成本和高峰负荷需求，提升能源管理效率。

Comments 51 pages, 7 figures, 11 tables, Preprint of the article published in Applied Energy: Shah, M.I.A., Victorio, M.E.C., Duffy, M., Barrett, E. and Mason, K. (2026). Peer-to-peer energy trading in dairy farms using multi-agent reinforcement learning. Applied Energy, 402, 127041. doi:10.1016/j.apenergy.2025.127041

Journal ref Applied Energy (2026), 402, 127041

Multi-Agent Collaborative Framework For Math Problem Generation

Kia Karbasi, Kevin Hong, Mohammad Amin Samadi, Gregory Pottie

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.CL

Comments Published in the Proceedings of the 18th International Conference on Educational Data Mining, 6 pages, 5 figures

Journal ref Kia Karbasi, Kevin Hong, Mohammad Amin Samadi, & Gregory Pottie. (2025). Multi-Agent Collaborative Framework For Math Problem Generation. Proceedings of the 18th International Conference on Educational Data Mining, 613--618

From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions

Changyuan Zhao, Ruichen Zhang, Jiacheng Wang, Dusit Niyato, Geng Sun, Xianbin Wang, Shiwen Mao, Abbas Jamalipour

机构 * College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学） ； College of Computer Science and Technology, Jilin University（计算机科学与技术学院，吉林大学） ； Department of Electrical and Computer Engineering, Western University（电气与计算机工程系，西方大学） ； Department of Electrical and Computer Engineering, Auburn University（电气与计算机工程系，阿伯茨罕大学） ； School of Electrical and Computer Engineering, University of Sydney（电气与计算机工程学院，悉尼大学） 纠错

专题命中 多智能体 ：agentic(title,abstract)；agent(abstract)；autonomous agent(abstract)；workflow(abstract)

Comments 7 pages, 4 figures

Establishing Shared Query Understanding in an Open Multi-Agent System

Nikolaos Kondylidis, Ilaria Tiddi, Annette ten Teije

机构 * Vrije Universiteit Amsterdam（阿姆斯特丹自由大学） 纠错

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；autonomous agent(comments,journal_ref)；分类 cs.AI

Comments 9 pages. International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), London, United Kingdom

Journal ref In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (AAMAS 23). International Foundation for Autonomous Agents and Multiagent Systems, Richland

A Large Language Model-based multi-agent manufacturing system for intelligent shopfloor

Zhen Zhao, Dunbing Tang, Changchun Liu, Liping Wang, Zequn Zhang, Haihua Zhu, Kai Chen, Qingwei Nie, Yuchen Ji

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Journal ref Zhao Z, Tang D, Liu C, et al. A Large language model-based multi-agent manufacturing system for intelligent shopfloors[J]. Advanced Engineering Informatics, 2026, 69: 103888

SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence

Yao Zhang, Chenyang Lin, Shijie Tang, Haokun Chen, Shijie Zhou, Yunpu Ma, Volker Tresp

专题命中 多智能体 ：agentic(title,abstract)；agent(abstract)；autonomous agent(abstract)；planning(abstract)

Comments 41 pages

Why do AI agents communicate in human language?

Pengcheng Zhou, Yinglun Feng, Halimulati Julaiti, Zhongliang Yang

机构 * Beijing University Of Posts and Telecommunications（北京邮电大学） 纠错

专题命中 多智能体 ：AI agent(title,abstract)；agent(abstract)；autonomous agent(abstract)；agentic(abstract)

Agentic AI and Multiagentic: Are We Reinventing the Wheel?

V. Botti

专题命中 多智能体 ：agentic(title,abstract)；agent(abstract)；AI agent(abstract)；autonomous agent(abstract)

Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning

Wenjun Zhang, Qiong Wu, Pingyi Fan, Kezhi Wang, Nan Cheng, Wen Chen, Khaled B. Letaief

机构 * School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院） ； Department of Electronic Engineering, State Key Laboratory of Space Network and Communications, and the Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学电子工程系、空间网络与通信国家重点实验室及北京信息科学与技术国家研究中心） ； Department of Computer Science, Brunel University（布鲁内尔大学计算机科学系） ； State Key Laboratory of ISN and the School of Telecommunications Engineering, Xidian University（西安电子科技大学信息与通信系统国家重点实验室及电信工程学院） ； Department of Electronic Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系） ； Department of Electrical and Computer Engineering, the Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系） 纠错

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.LG

Comments This paper has been submitted to IEEE Journal. The source code has been released at:https://github.com/qiongwu86/Semantic-Aware-Resource-Management-for-C-V2X-Platooning-via-Multi-Agent-Reinforcement-Learning

Learning Multi-agent Multi-machine Tending by Mobile Robots

Abdalwhab Abdalwhab, Giovanni Beltrame, Samira Ebrahimi Kahou, David St-Onge

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.LG

Comments 8 pages, 4 figures, Accepted at an AAAI workshop (The Multi-Agent AI in the Real World Workshop)

Learning Reward Machines in Cooperative Multi-Agent Tasks

Leo Ardon, Daniel Furelos-Blanco, Alessandra Russo

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Comments Neuro-symbolic AI for Agent and Multi-Agent Systems Workshop at AAMAS'23

Multi-Agent Training for Pommerman: Curriculum Learning and Population-based Self-Play Approach

Nhat-Minh Huynh, Hoang-Giang Cao, I-Chen Wu

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Comments Accepted at The First Workshop on Game AI Algorithms and Multi-Agent Learning - IJCAI 2024

A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data

Aniruddha Salve, Saba Attar, Mahesh Deshmukh, Sayali Shivpuje, Arnab Mitra Utsab

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Comments 16 pages, 3 figures. This preprint introduces a multi-agent framework for Retrieval-Augmented Generation (RAG), enhancing Large Language Models (LLMs) for efficient integration of diverse data sources. Relevant for researchers in AI, ML, generative AI, and database systems

Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking

Valeria Jannelli, Stefan Schoepf, Matthias Bickel, Torbjørn Netland, Alexandra Brintrup

专题命中 多智能体 ：agent(title)；agentic(title)；multi-agent(title)；分类 cs.AI

Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning

Wenhua Wang, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, Khaled B. Letaief

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.LG

Comments This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learning

SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning

Qian Long, Fangwei Zhong, Mingdong Wu, Yizhou Wang, Song-Chun Zhu

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Comments AAAI 2024 Cooperative Multi-Agent Systems Decision-Making and Learning (CMASDL) Workshop

Cooperation Dynamics in Multi-Agent Systems: Exploring Game-Theoretic Scenarios with Mean-Field Equilibria

Vaigarai Sathi, Sabahat Shaik, Jaswanth Nidamanuri

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Comments Accepted for MADGames: Multi-Agent Dynamic Games Workshop at IROS 2023, see details at https://iros2023-madgames.f1tenth.org/proceedings.html

Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning

Barna Pásztor, Ilija Bogunovic, Andreas Krause

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.LG

Journal ref Pásztor, B., Krause, A., & Bogunovic, I. (2023). Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning. Transactions on Machine Learning Research

Variational Policy Propagation for Multi-agent Reinforcement Learning

Chao Qu, Hui Li, Chang Liu, Junwu Xiong, James Zhang, Wei Chu, Weiqiang Wang, Yuan Qi, Le Song

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.LG

Comments The title of previous version was "Intention Propagation for Multi-agent Reinforcement Learning"

Multi-Agent Simulation for AI Behaviour Discovery in Operations Research

Michael Papasimeon, Lyndon Benke

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Comments 14 pages, 7 figures. To be published in proceedings of the 22nd International Workshop on Multi-Agent-Based Simulation (MABS 2021) at AAMAS 2021. accepted/" target="_blank" rel="noopener">https://mabsworkshop.github.io/accepted/

Decomposability and Parallel Computation of Multi-Agent LQR

Gangshan Jing, He Bai, Jemin George, Aranya Chakrabortty

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Comments This paper contains proofs of all the theorems in the conference paper "Decomposability and Parallel Computation of Multi-Agent LQR"

Hybrid Information-driven Multi-agent Reinforcement Learning

William A. Dawson, Ruben Glatt, Edward Rusu, Braden C. Soper, Ryan A. Goldhahn

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Comments Published at Workshop on Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL AAAI 2021). This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. Lawrence Livermore National Security, LLC through the support of LDRD 20-SI-005. LLNL-CONF-816423

Multi-Agent Tensor Fusion for Contextual Trajectory Prediction

Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris Baker, Yibiao Zhao, Yizhou Wang, Ying Nian Wu

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.LG

Comments Presented in CVPR 19: http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Multi-Agent_Tensor_Fusion_for_Contextual_Trajectory_Prediction_CVPR_2019_paper.html ; Architecture details available: https://github.com/programmingLearner/MATF-architecture-details

Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios

Hang Ma, Sven Koenig, Nora Ayanian, Liron Cohen, Wolfgang Hoenig, T. K. Satish Kumar, Tansel Uras, Hong Xu, Craig Tovey, Guni Sharon

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Comments In IJCAI-16 Workshop on Multi-Agent Path Finding

Tableau-based decision procedure for the multi-agent epistemic logic with all coalitional operators for common and distributed knowledge

Mai Ajspur, Valentin Goranko, Dmitry Shkatov

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Comments Substantially extended and corrected version of arXiv:0902.2125. To appear in: Logic Journal of the IGPL, special issue on Formal Aspects of Multi-Agent Systems

A new approach of designing Multi-Agent Systems

Sara Maalal, Malika Addou

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI

Comments 10 pages, 12 figures, A practical application of a method of designing multi-agent systems based on the AUML language and the MDA approach at "the 4th IEEE Workshop on Information Technologies and Communication (WOTIC'11)", Casablanca, 13 - 15 October 2011, International Journal of Advanced Computer Science and Applications(IJACSA) Volume 2 No. 11 November 2011

The Impact of Social Networks on Multi-Agent Recommender Systems

Hamilton Link, Jared Saia, Terran Lane, Randall A. LaViolette

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.LG

Comments 12 pages, 4 figures. Published in the Proceedings of the Workshop on Cooperative Multi-Agent Learning (ECML/PKDD '05). Resubmitted to fix citations and metadata

Agentic Large Language Models, a survey

代理大语言模型：综述

Aske Plaat, Max van Duijn, Niki van Stein, Mike Preuss, Peter van der Putten, Kees Joost Batenburg

机构 * Leiden University Leiden Netherlands ； Leiden University \& AI Lab, Pegasystems Leiden Netherlands ； Leiden University ； Leiden University \& AI Lab, Pegasystems 纠错

专题命中 多智能体 ：agentic(title,abstract)；agent(abstract)；tool use(abstract)；multi-agent(abstract)

AI总结 本文综述了代理大语言模型的研究现状，探讨了其在医疗诊断、物流和金融分析等领域的应用，并提出通过推理、行动和交互提升大语言模型能力的未来研究方向。

Comments Website: https://askeplaat.github.io/agentic-llm-survey-site/

Journal ref JAIR volume 84, article 29, December 2025

On Some Fundamental Problems for Multi-Agent Systems Over Multilayer Networks

Daniel J. Rosenkrantz, Madhav V. Marathe, Zirou Qiu, S. S. Ravi, Richard E. Stearns

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；autonomous agent(comments)

Comments This is a complete version of the paper (with the same title) that will appear in Proc. of the 24th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2025), Detroit, MI, May 2025

Analysing Factorizations of Action-Value Networks for Cooperative Multi-Agent Reinforcement Learning

Jacopo Castellini, Frans A. Oliehoek, Rahul Savani, Shimon Whiteson

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；autonomous agent(comments)

Comments This work as been accepted as an Extended Abstract in Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), N. Agmon, M. E. Taylor, E. Elkind, M. Veloso (eds.), May 2019, Montreal, Canada

Journal ref Auton Agent Multi-Agent Syst 35, 25 (2021)