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.

该教师早期职业发展（职业）奖为研究和教育活动提供了基金，以优化的共同主题。研究目标是为大规模分布的多代理系统建立新的计算模型，理论进步和优化算法。感兴趣的是由具有不同性能标准的多个互连代理组成的系统。由于各种原因，例如私人或专有信息，代理商没有共享自己的目标，而是分享稀缺的资源，并希望合作实现一个共同的目标。在没有中央协调员或中央信息访问的情况下，必须分布此类多代理系统的协调和优化。一个主要的研究目标是开发和研究数学模型，并设计和分析分布式的多试算法。在分布式模型中，每个代理都在本地行动，并与邻居共享一些有限的信息，而代理连接随着时间的推移动态变化。另一个目的是探索和量化系统各个特征（例如存在通信噪声或延迟）下算法的性能极限。 算法开发需要一些基本研究，为系统性能的分析和表征提供了新的数学工具。这项研究与教育计划紧密相关，以建立新的本科和毕业生优化课程，以使学生获得有效和系统地识别，模型，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对妇女和其他代表性不足的群体的兴趣，以及对优化的重要性和美感进行宣传和教育。成功完成研究活动的完成将导致新的分散式协调和优化算法的新有效设计。此外，这将导致全球优化算法的设计，并保证大量非线性非凸问题的性能。总体而言，结果将增强一般优化的现有知识。计划的教育活动将促进优化并增强学生群体的多样性。