Intelligent agents offer a new and exciting way of understanding the world of work. Agent-Based Simulation (ABS), one way of using intelligent agents, carries great potential for progressing our understanding of management practices and how they link to retail performance. We have developed simulation models based on research by a multi-disciplinary team of economists, work psychologists and computer scientists. We will discuss our experiences of implementing these concepts working with a well-known retail department store. There is no doubt that management practices are linked to the performance of an organisation (Reynolds et al., 2005; Wall & Wood, 2005). Best practices have been developed, but when it comes down to the actual application of these guidelines considerable ambiguity remains regarding their effectiveness within particular contexts (Siebers et al., forthcoming a). Most Operational Research (OR) methods can only be used as analysis tools once management practices have been implemented. Often they are not very useful for giving answers to speculative 'what-if' questions, particularly when one is interested in the development of the system over time rather than just the state of the system at a certain point in time. Simulation can be used to analyse the operation of dynamic and stochastic systems. ABS is particularly useful when complex interactions between system entities exist, such as autonomous decision making or negotiation. In an ABS model the researcher explicitly describes the decision process of simulated actors at the micro level. Structures emerge at the macro level as a result of the actions of the agents and their interactions with other agents and the environment. 3 We will show how ABS experiments can deal with testing and optimising management practices such as training, empowerment or teamwork. Hence, questions such as "will staff setting their own break times improve performance?" can be investigated.
AgentSafe: Safeguarding Large Language Model-based Multi-agent Systems via Hierarchical Data Management
University of Science and Technology of China（中国科学技术大学）；Nanyang Technological University（南洋理工大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Squirrel Ai Learning
Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distribution
Privacy-Aware Agent Collaboration for Dynamic VR Slice Management in 6G SD-RAN
CCAS Department, School of Engineering, NewGiza University (NGU)（计算机与软件工程学院，新吉扎大学）；Electronics and Communications, Helwan Univ. Cairo, Egypt（电子与通信，开罗海威大学，埃及）
An Intelligent Approach for Negotiating between chains in Supply Chain Management Systems
在供应链管理系统中进行商业谈判和选择最佳供应商是生产商在生产过程中的弱点之一。因此，应用智能系统可能会有一个有效的作用，在加快速度和提高质量的选择。本文介绍了一个系统，试图使用多代理系统和任何代理之间举行谈判的交易。在该系统中，每一个链段都有一个智能Agent，它通过协商媒介参与发送命令和接收响应，并与其他Agent进行通信，本文介绍了Agent之间的通信方式、多Agent的特点以及环境中每个Agent的标准注册媒介。利用JADE（Java Application Development Environment）实现和模拟了Agent之间的协作。
SWE-MeM: Learning Adaptive Memory Management for Long-Horizon Coding Agents
The Chinese University of Hong Kong, China（香港中文大学）；Shanghai Jiao Tong University, China（上海交通大学）；Tsinghua University, China（清华大学）；ByteDance, China（字节跳动）
Towards an efficient and risk aware strategy for guiding farmers in identifying best crop management
Univ Montpellier（蒙彼利埃大学）；CIRAD（法国国际农业发展研究中心）；Alliance of Bioversity International and CIAT（国际生物多样性中心与国际热带农业中心联盟）；Univ. Lille（里尔大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Centrale Lille（里尔中央理工学院）；INRAE（法国国家农业、食品与环境研究院）；Institut Agro（法国国立高等农业学院）；Institut National de l’Environnement et de Recherches Agricoles (INERA)（国家环境与农业研究所）；International Maize and Wheat Improvement Centre (CIMMYT)（国际玉米小麦改良中心）；International Institute of Tropical Agriculture（国际热带农业研究所）
Learning Agent-Compatible Context Management for Long-Horizon Tasks
Renmin University of China（中国人民大学）；Tongyi Lab, Alibaba Group（阿里云实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Université de Montréal（蒙特利尔大学）
Towards Explainability for a Civilian UAV Fleet Management using an Agent-based Approach
CIAD, Univ. Bourgogne Franche-Comté, UTBM（勃艮第弗朗什-孔泰大学技术大学分校复杂系统与人工智能研究所）；AI-Robolab/ICR, Computer Science and Communications, University of Luxembourg（卢森堡大学计算机科学与通信系人工智能机器人实验室/交互与机器人研究所）；Department of Computing Science, Umeȧ University（于默奥大学计算机科学系）；University of Ngaoundere（恩冈代雷大学）；CIAD, Univ. Bourgogne Franche-Comté, UB（勃艮第弗朗什-孔泰大学复杂系统与人工智能研究所）
AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
International Business Machines (IBM)（国际商业机器公司（IBM））；Global Atlantic Financial（全球大西洋金融公司）；Docusign（DocuSign公司）；Salesforce Inc（Salesforce公司）；The Home Depot（家得宝公司）
Formal Foundations of Agentic Business Process Management
University of Oxford, UK（牛津大学，英国）；SAP, Germany（德国SAP公司）；Umeå University, Sweden（乌梅拉大学，瑞典）；University of Mannheim, Germany（曼海姆大学，德国）；Free University of Bozen-Bolzano, Italy（博兹纳自由大学，意大利）
MemLens: A Value-Aware Memory Management System with Interactive Analytics for LLM-based Agents
C-FAIR & School of Software, Shandong University（C-FAIR与山东大学软件学院）；SKLCCSE Lab, Beihang University（北京航空航天大学SKLCCSE实验室）；Department of Data Science, City University of Hong Kong（香港城市大学数据科学系）
