In the realm of multi-agent systems, the challenge of \emph{partial observability} is a critical barrier to effective coordination and decision-making. Existing approaches, such as belief state estimation and inter-agent communication, often fall short. Belief-based methods are limited by their focus on past experiences without fully leveraging global information, while communication methods often lack a robust model to effectively utilize the auxiliary information they provide. To solve this issue, we propose Global State Diffusion Algorithm~(GlobeDiff) to infer the global state based on the local observations. By formulating the state inference process as a multi-modal diffusion process, GlobeDiff overcomes ambiguities in state estimation while simultaneously inferring the global state with high fidelity. We prove that the estimation error of GlobeDiff under both unimodal and multi-modal distributions can be bounded. Extensive experimental results demonstrate that GlobeDiff achieves superior performance and is capable of accurately inferring the global state.

翻译：在多智能体系统领域，\emph{部分可观测性}的挑战是阻碍有效协调与决策的关键障碍。现有方法，如信念状态估计和智能体间通信，往往存在不足。基于信念的方法受限于其对过往经验的关注，未能充分利用全局信息；而通信方法则通常缺乏有效利用其所提供辅助信息的鲁棒模型。为解决此问题，我们提出全局状态扩散算法（GlobeDiff），以基于局部观测推断全局状态。通过将状态推断过程建模为多模态扩散过程，GlobeDiff克服了状态估计中的模糊性，同时以高保真度推断全局状态。我们证明了GlobeDiff在单模态与多模态分布下的估计误差均存在上界。大量实验结果表明，GlobeDiff实现了优越的性能，并能够准确推断全局状态。