基于高斯过程的去中心化标量场映射
Decentralized Scalar Field Mapping using Gaussian Process
AI总结：
本文提出去中心化交集数据共享与同化协议，通过局部高斯过程模型和子域重叠几何构造邻居特定包，提升团队预测性能。
摘要：
去中心化高斯过程（GP）方法通过将集中式全局模型替换为多个由个体代理维护的局部模型，为多智能体标量场估计提供可扩展框架。本文研究是否能系统利用智能体间后验不一致来提升团队预测性能，并通过新颖的去中心化交集数据共享与同化协议给出肯定回答。具体而言，每个代理利用其局部GP与子域重叠几何构造邻居特定包，并选择性同化来自邻居的信息以提升共享区域的后验一致性。所提架构在计算和通信上保持局部性，支持去中心化邻居间数据同化，并允许本地GP模型在网络中协同进化，无需全包交换或集中推断。
英文摘要：
Decentralized Gaussian process (GP) methods offer a scalable framework for multi-agent scalar-field estimation by replacing a centralized global model with multiple local models maintained by individual agents. A team of agents operates through overlapping domains; neighboring agents generally produce inconsistent distributions over shared regions. This paper investigates whether these inter-agent posterior discrepancies can be systematically exploited to improve team-level predictive performance and answers this question positively through a novel decentralized intersection data-sharing and assimilation protocol. Specifically, each agent constructs neighbor-specific packets from its local GP together with the geometry of the overlap between subdomains and selectively assimilates information received from neighboring agents to improve consistency of its posterior over the shared regions. The proposed architecture preserves locality in both computation and communication, supports decentralized neighbor-to-neighbor data assimilation, and allows local GP models to evolve cooperatively across the network without requiring the exchange full packet exchange or centralized inference.
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