We introduce a novel framework, Online Relational Inference (ORI), designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems using streaming data. Unlike traditional offline methods that rely on a fixed training set, ORI employs online backpropagation, updating the model with each new data point, thereby allowing it to adapt to changing environments in real-time. A key innovation is the use of an adjacency matrix as a trainable parameter, optimized through a new adaptive learning rate technique called AdaRelation, which adjusts based on the historical sensitivity of the decoder to changes in the interaction graph. Additionally, a data augmentation method named Trajectory Mirror (TM) is introduced to improve generalization by exposing the model to varied trajectory patterns. Experimental results on both synthetic datasets and real-world data (CMU MoCap for human motion) demonstrate that ORI significantly improves the accuracy and adaptability of relational inference in dynamic settings compared to existing methods. This approach is model-agnostic, enabling seamless integration with various neural relational inference (NRI) architectures, and offers a robust solution for real-time applications in complex, evolving systems.

翻译：我们提出了一种新颖的框架——在线关系推断（ORI），旨在利用流数据高效识别演化多智能体交互系统中的隐藏交互图。与依赖固定训练集的传统离线方法不同，ORI采用在线反向传播，随每个新数据点更新模型，从而使其能够实时适应变化的环境。一个关键创新是将邻接矩阵作为可训练参数，并通过一种名为AdaRelation的新型自适应学习率技术进行优化，该技术根据解码器对交互图变化的历史敏感性进行调整。此外，我们引入了一种称为轨迹镜像（TM）的数据增强方法，通过使模型接触多样化的轨迹模式来提升泛化能力。在合成数据集和真实世界数据（用于人体运动的CMU MoCap）上的实验结果表明，与现有方法相比，ORI在动态环境中显著提高了关系推断的准确性和适应性。该方法与模型无关，能够与各种神经关系推断（NRI）架构无缝集成，并为复杂演化系统中的实时应用提供了稳健的解决方案。