发表机构
Shenzhen University（深圳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多智能体系统中隐藏智能体轨迹与交互的联合恢复难题，提出SIHA方法，结合结构无关初始化与结构引导迭代细化，在基准系统上显著提升结构推断、隐藏状态重建与未来预测性能。
AI 中文摘要
从多智能体动力学中恢复潜在交互结构对于理解和预测交互系统至关重要。基于轨迹的结构推断已取得令人满意的性能，但传统公式假设所有建模智能体的轨迹都是可用的。在实践中，由于感知受限、遮挡或通信故障，智能体在部署时可能变得不可观测。现有研究已考虑了未见节点估计、部分观测下的结构推断以及缺失值插补，但隐藏智能体轨迹及其交互的联合恢复仍未得到充分探索。我们将此问题表述为隐藏智能体下的结构推断。其关键难点在于循环依赖：恢复涉及隐藏智能体的交互需要对其轨迹进行估计，而轨迹重建本身也能受益于结构信息。为应对这一挑战，我们提出了隐藏智能体下的结构推断（SIHA），该方法将结构无关的初始化与结构引导的迭代细化相结合。SIHA从可见观测中重建隐藏轨迹，使用神经关系推断来推断交互，并通过多强度结构注意力和迭代状态-结构更新将估计的结构反馈到隐藏状态重建中。在三个基准动力系统上的实验表明，可见到可见的结构推断取得了一致的改进，同时在隐藏状态重建和未来预测方面也显示出优势。具有模拟全身遮挡的运动捕捉实验进一步证明了其在现实隐藏智能体设置中的有效性。
英文摘要
Recovering latent interaction structures from multi-agent dynamics is important for understanding and predicting interacting systems. Trajectory-based structural inference has achieved promising performance, but conventional formulations assume that the trajectories of all modeled agents are available. In practice, agents may become unobserved at deployment because of limited sensing, occlusion, or communication failure. Existing studies have considered unseen-node estimation, structural inference under partial observations, and missing-value imputation, yet the joint recovery of hidden-agent trajectories and their interactions remains underexplored. We formulate this problem as structural inference under hidden agents. Its key difficulty is a circular dependency: recovering interactions involving a hidden agent requires an estimate of its trajectory, while trajectory reconstruction can itself benefit from structural information. To address this challenge, we propose Structural Inference under Hidden Agents (SIHA), which combines structure-agnostic initialization with structure-guided iterative refinement. SIHA reconstructs hidden trajectories from visible observations, infers interactions using Neural Relational Inference, and feeds the estimated structure back into hidden-state reconstruction through multi-strength structural attention and iterative state--structure updates. Experiments on three benchmark dynamical systems demonstrate consistent improvements in visible-to-visible structural inference, while also showing benefits in hidden-state reconstruction and future prediction. Motion-capture experiments with simulated whole-limb occlusion further demonstrate its effectiveness in realistic hidden-agent settings.
