Cloud-based optimization: A quasi-decentralized approach to multi-agent coordination
Cloud-based optimization: A quasi-decentralized approach to multi-agent coordination
复制标题
基于云的优化：一种准分散的多智能体协调方法
DOI：
10.1109/cdc.2014.7040430
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Egerstedt
中科院分区：
文献类型：
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作者：
M. Hale;M. Egerstedt
需要新的架构和算法来反映多代理系统通过云连接时可用的本地和全局信息的混合。我们提出了一种新的架构，多代理协调云被假定为能够收集来自所有代理的信息，执行集中计算，并以间歇性的方式传播的结果。这种架构是用来解决多智能体优化问题，其中每个代理有一个本地目标函数未知的其他代理和代理集体受到全球不等式约束。利用云，一个对偶问题制定和解决找到一个鞍点的相关拉格朗日。
New architectures and algorithms are needed to reflect the mixture of local and global information that is available as multi-agent systems connect over the cloud. We present a novel architecture for multi-agent coordination where the cloud is assumed to be able to gather information from all agents, perform centralized computations, and disseminate the results in an intermittent manner. This architecture is used to solve a multi-agent optimization problem in which each agent has a local objective function unknown to the other agents and in which the agents are collectively subject to global inequality constraints. Leveraging the cloud, a dual problem is formulated and solved by finding a saddle point of the associated Lagrangian.