New Techniques for the Control of Multi-Agent Systems in Uncertain Environments
New Techniques for the Control of Multi-Agent Systems in Uncertain Environments
批准号:
0500234
负责人:
Venkatachalam Anantharam
金额:
$30.46万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2005
资助国家:
美国
项目状态:
已结题
起止时间:
2005-07-01 至 2009-06-30
中文摘要
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英文摘要
New techniques for the control of multi-agent systems in uncertain environments"ABSTRACTThe research develops new techniques for the control of multi-agent systems in uncertain environments. The problem area is of critical national and scientific importance, since modern engineered systems deployed for many critical applications are comprised of multiple agents who need to coordinate their actions to achieve a control objective in an uncertain environment. Such systems are used for many applications, including surveillance, environmental monitoring, inventory control, and control in hazardous environments, among many others, in fields such as transportation, medical diagnostics, civil engineering, communications networks, and defense systems.The main scientific ideas being developed are (1) a new technique to tame the complexity of controlling multi-agent systems by a focus on aggregate behavior, i.e. not so much on which agent carries out a given action as on how many agents carry out a given action, and (2) a new technique to based on the use of common randomness between the agents to increase their ability to hedge against uncertainty in the environment.The aggregation techniques to tame complexity that are being developed rely on mean-field methods, which have proven well-suited to exposing phase transitions in physical systems, but have so far not been systematically used for control. The phenomenon of symmetry breaking, which occurs when different portions of a homogeneous system must use different control rules to obtain optimal system wide performance, is of particular interest. The techniques to hedge against uncertainty that are being developed rely on game-theoretic models, as well as methods to extract common randomness from ambient noise and the randomized choices of other agents.
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