CAREER: Cooperative Multi-Agent Optimization

职业：协作多智能体优化

• 批准号: 0742538
• 负责人: Angelia Nedich
• 金额: $ 40万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-02-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0742538&HistoricalAwards=false
• 关键词: CAREER; Cooperative; Multi; Agent; Optimization

This Faculty Early Career Development (CAREER) award provides funds for research and education activities on a common theme of optimization. The research objective is to establish new computational models, theoretical advances, and optimization algorithms for large scale distributed multi-agent systems. Of interest are systems that consist of interconnected multiple agents with different performance criteria. For various reasons, such as private or proprietary information, the agents do not share their own objectives, but do share scarce resources and want to cooperatively achieve a common goal. In the absence of a central coordinator or central information access, the coordination and optimization of such multi-agent systems have to be distributed. A primary research objective is to develop and study mathematical models, and design and analyze distributed multi-agent algorithms. In the distributed model, each agent acts locally and shares some limited information with its neighbors while the agent connectivity is dynamically changing with time. Another objective is to explore and quantify the performance limits of the algorithms under various characteristics of the system, such as the presence of communication noise or delays. The algorithmic development necessitates some fundamental research providing new mathematical tools for analysis and characterization of the system performance. The research is closely tied with educational plans to build new undergraduate and graduate optimization courses to equip the students with the knowledge to recognize, model, analyze, and solve optimization problems efficiently and systematically. A broader educational goal includes promoting interest for women and other under-represented groups in optimization, as well as outreaching and educating young minds about the significance and beauty of optimization.Successful completion of the research activities will lead to new efficient designs of decentralized coordination and optimization algorithms for large network systems. Also, it will lead to the designs of global optimization algorithms with guaranteed performance for a large class of non-linear non-convex problems. Overall, the results will enhance the existing knowledge in optimization in general. The planned educational activities will promote optimization and enhance the diversity in the student population.

这个教师早期职业发展（CAREER）奖为优化的共同主题的研究和教育活动提供资金。研究目标是建立新的计算模型，理论进展和优化算法的大规模分布式多智能体系统。感兴趣的是由具有不同性能标准的互连多个代理组成的系统。由于各种原因，例如私人或专有信息，代理人不共享自己的目标，但共享稀缺资源并希望合作实现共同目标。在没有中央协调器或中央信息访问的情况下，此类多代理系统的协调和优化必须是分布式的。一个主要的研究目标是开发和研究数学模型，设计和分析分布式多智能体算法。在分布式模型中，每个代理都在本地活动，并与其邻居共享有限的信息，而代理的连通性随时间动态变化。另一个目标是探索和量化的算法的性能限制下的各种特性的系统，如通信噪声或延迟的存在。 算法的发展需要一些基础研究，为系统性能的分析和表征提供新的数学工具。该研究与教育计划紧密联系，以建立新的本科生和研究生优化课程，使学生具备有效和系统地识别，建模，分析和解决优化问题的知识。一个更广泛的教育目标包括促进妇女和其他代表性不足的群体对优化的兴趣，以及外展和教育年轻人关于优化的意义和美感。研究活动的成功完成将导致大型网络系统的分散协调和优化算法的新的有效设计。此外，它将导致一个大类的非线性非凸问题的全局优化算法的设计与保证性能。总的来说，结果将提高现有的知识在优化一般。计划中的教育活动将促进优化和加强学生群体的多样性。