International Journal of Computer Science Issues, IJCSI Volume 5, pp33-37, October 2009
发表信息：
R. Sindhu, A. W. and G.N.Purohit, "Multi-Agent System Interaction in Integrated SCM", International Journal of Computer Science Issues, IJCSI, Volume 5, pp33-37, October 2009
Coordination between organizations on strategic, tactical and operation levels leads to more effective and efficient supply chains. Supply chain management is increasing day by day in modern enterprises. The environment is becoming competitive and many enterprises will find it difficult to survive if they do not make their sourcing, production and distribution more efficient. Multi-agent supply chain management has recognized as an effective methodology for supply chain management. Multi-agent systems (MAS) offer new methods compared to conventional, centrally organized architectures in the scope of supply chain management (SCM). Since necessary data are not available within the whole supply chain, an integrated approach for production planning and control taking into account all the partners involved is not feasible. In this study we show how MAS architecture interacts in the integrated SCM architecture with the help of various intelligent agents to highlight the above problem.
Causal Mean Field Multi-Agent Reinforcement Learning
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Nanjing, University of Chinese Academy of Sciences（中国科学院大学南京学院）；Alibaba Group（阿里巴巴集团）
SHM-Agents: A Generalist-Specialist Integrated Agent System for Structural Health Monitoring
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin（工业和信息化部智能防灾减灾重点实验室，哈尔滨工业大学，哈尔滨）；Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin（教育部结构动态行为与控制重点实验室，哈尔滨工业大学，哈尔滨）；School of Civil Engineering, Harbin Institute of Technology, Harbin（哈尔滨工业大学土木工程学院）
IFPV: An Integrated Multi-Agent Framework for Generative Operational Planning and High-Fidelity Plan Verification
School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；Engineering Research Center of Intelligent Swarm Systems, Ministry of Education（教育部智能群体系统工程研究中心）；National Supercomputing Center in Zhengzhou（郑州国家超算中心）；Henan Research Center for Large Model Technology（河南省大模型技术与新质软件工程研究中心）
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks
School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC
Zhejiang University（浙江大学）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Zhejiang Provincial Key Laboratory of Info. Proc., Commun. & Netw. (IPCAN)（浙江省信息处理、通信与网络重点实验室）；King’s College London（伦敦国王学院）；Department of Engineering, King’s College London（伦敦国王学院工程系）；University of Miami（迈阿密大学）；Institute for Data Science and Computing, University of Miami（迈阿密大学数据科学与计算研究所）；The University of Hong Kong（香港大学）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子电气工程系）
