论文标题
消息传递在基于代理的supdodular最大化中的影响
The Impact of Message Passing in Agent-Based Submodular Maximization
论文作者
论文摘要
本文考虑了一组传感器，该传感器的任务是对环境进行测量，并将测量值的一小部分发送到集中的数据融合中心，其中测量将用于估计环境的整体状态。传感器的目标是发送最有用的测量集，以使估计值尽可能准确。该问题被表述为一个次模化问题，存在一个良好的贪婪算法，每个传感器都从其本地集合中依次选择一组测量值，并将其决策传达给序列中的未来传感器。在这项工作中，传感器可以互相共享测量，以增强每个传感器的决策集。我们探讨如何利用这种交流的增加来改善名义贪婪算法的结果。具体而言，我们表明，这种测量通过可以提高所得测量的质量，最高为$ n+1 $，其中$ n $是传感器的数量。
This paper considers a set of sensors, which as a group are tasked with taking measurements of the environment and sending a small subset of the measurements to a centralized data fusion center, where the measurements will be used to estimate the overall state of the environment. The sensors' goal is to send the most informative set of measurements so that the estimate is as accurate as possible. This problem is formulated as a submodular maximization problem, for which there exists a well-studied greedy algorithm, where each sensor sequentially chooses a set of measurements from its own local set, and communicates its decision to the future sensors in the sequence. In this work, sensors can additionally share measurements with one another, in order to augment the decision set of each sensor. We explore how this increase in communication can be exploited to improve the results of the nominal greedy algorithm. Specifically, we show that this measurement passing can improve the quality of the resulting measurement set by up to a factor of $n+1$, where $n$ is the number of sensors.
