Gossiping（闲聊）在网络化智能体信息传播中的作用
The Role of Gossiping for Information Dissemination over Networked Agents
AI总结：
本文研究网络化智能体中的闲聊信息传播，提出平均误差度量与自适应源传输容量策略，刻画高低闲聊速率行为并求得最优速率。
摘要：
我们研究由 gossiping（闲聊）智能体（节点）组成的网络中的信息传播问题。在该模型中，信息源持续保存关于时变二元世界状态的最新信息，而 n 个接收节点希望尽可能准确地跟随信息源处的信息。当信息源处的信息发生变化时，信息源首先向 m≤n 个节点的一个子集发送更新。此后，各节点在闲聊期间共享其本地信息，以进一步传播信息。随后，节点在闲聊期结束时使用多数决规则估计信息源处的信息。为分析信息传播，我们引入一种新的误差度量，用于求得能够准确获得信息源最新信息的节点的平均百分比。我们刻画了获得平均误差稳态分布所需的方程，随后分析系统在高闲聊速率和低闲聊速率下的行为。在高闲聊速率下，即每个节点能够更频繁地访问其他节点的信息时，我们证明节点基于网络中信息的多数来更新其信息。在低闲聊速率下，我们引入并分析闲聊增益，即由闲聊带来的平均误差降低。特别地，我们提出一种自适应策略，信息源可利用该策略，根据其过去的传输速率以及节点处信息的准确性来确定当前传输容量 m。数值结果表明，当信息源的传输容量 m 受限时，闲聊可能是有害的，因为它会导致错误信息传播。随后，对于固定的 m，我们找到使平均误差最小化的最优闲聊速率。最后，我们说明即使在高闲聊速率下，与恒定 m 选择策略相比，我们的自适应策略仍具有更优性能。
英文摘要：
We consider information dissemination over a network of gossiping agents (nodes). In this model, a source keeps the most up-to-date information about a time-varying binary state of the world, and $n$ receiver nodes want to follow the information at the source as accurately as possible. When the information at the source changes, the source first sends updates to a subset of $m\leq n$ nodes. After that, the nodes share their local information during the gossiping period to disseminate the information further. The nodes then estimate the information at the source using the majority rule at the end of the gossiping period. To analyze information dissemination, we introduce a new error metric to find the average percentage of nodes that can accurately obtain the most up-to-date information at the source. We characterize the equations necessary to obtain the steady-state distribution for the average error and then analyze the system behavior under both high and low gossip rates. In the high gossip rate, in which each node can access other nodes' information more frequently, we show that the nodes update their information based on the majority of the information in the network. In the low gossip rate, we introduce and analyze the gossip gain, which is the reduction at the average error due to gossiping. In particular, we develop an adaptive policy that the source can use to determine its current transmission capacity $m$ based on its past transmission rates and the accuracy of the information at the nodes. In numerical results, we show that when the source's transmission capacity $m$ is limited, gossiping can be harmful as it causes incorrect information to disseminate. We then find the optimal gossip rates to minimize the average error for a fixed $m$. Finally, we illustrate the outperformance of our adaptive policy compared to the constant $m$-selection policy even for the high gossip rates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
