Scaling in Domains with Uncertainty: Criticality-Sensitive Coordination
msra(2006)
摘要
We consider a team of agents that are required to coordinate
their actions in order to maximize a global objective.
Our domains are characterized by uncertainty, dynamism,
and distributed information. Determining appropriate actions
becomes quite difficult, especially as the number of
agents and the coupling between them increases. This paper
discusses five contributions toward the goal of coordinating
agents in large-scale settings: (i) an approach based
on identifying the criticality of various activities with respect
to their effect on the team reward; (ii) an architecture
and implemented coordinator agent that can execute this
approach in a distributed manner; (iii) a vast suite of visualization
tools that considerably aid the challenging task of
monitoring and debugging; (iv) metrics for evaluating system
performance in such settings, and (v) a proof of concept
of our approach on both focused and randomly-generated
experimental domains.
更多查看译文
关键词
reasoning under uncertainty,large-scale systems,system performance,proof of concept
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要