发表机构
Torch Technologies（Torch Technologies）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多智能体标量场估计中局部GP后验不一致的问题，提出IPD-GP框架，通过发送方选择诱导点并传输后验信息，接收方选择性吸收为虚拟测量，在保持分布式的同时提升一致性并减少通信。
AI 中文摘要
分布式高斯过程（GP）方法通过允许每个智能体在其分配的子域上维护一个局部GP，为多智能体标量场估计提供了可扩展的框架。由于相邻智能体基于不同的测量值来调节其GP模型，它们的GP后验在重叠区域通常不一致。本文提出了诱导点分布式高斯过程（IPD-GP）框架，利用这些差异来提高共享区域上的后验一致性。每个发送方通过选择针对成对重叠区域的诱导位置，并传输相应的后验均值和方差，来构建接收方特定的数据包。每个接收方随后选择性地将信息丰富的包作为持久性虚拟测量值纳入其局部GP中。所开发的架构保持了分布式计算和通信，无需完整数据集交换或集中式推理。性能通过归一化均方根误差、负对数预测密度及其重叠限制对应指标进行评估，并与非通信局部GP、分布式聚合方法和集中式GP进行比较。仿真结果表明，IPD-GP在实现竞争性预测精度的同时减少了智能体间的通信。
英文摘要
Decentralized Gaussian process (GP) methods provide a scalable framework for multi-agent scalar-field estimation by allowing each agent to maintain a local GP over its assigned subdomain. Because neighboring agents condition their GP models on different measurements, their GP posteriors generally disagree over overlapping regions. This paper introduces the inducing points decentralized Gaussian process (IPD-GP) framework, which exploits these discrepancies to improve posterior consistency over shared regions. Each sender constructs receiver-specific data packets by selecting inducing locations targeted at the pairwise overlap and transmitting the corresponding posterior means and variances. Each receiver then selectively assimilates informative packets as persistent fictitious measurements in its local GP. The developed architecture preserves decentralized computation and communication without requiring full dataset exchange or centralized inference. Performance is evaluated using normalized root-mean-square error, negative log predictive density, and their overlap-restricted counterparts, with comparisons against non-communicating local GPs, decentralized aggregation methods, and a centralized GP. Simulation results show that IPD-GP achieves competitive predictive accuracy while reducing inter-agent communication.
