From pedestrians to Kuramoto oscillators, interactions between agents govern how a multitude of dynamical systems evolve in space and time. Discovering how these agents relate to each other can improve our understanding of the often complex dynamics that underlie these systems. Recent works learn to categorize relationships between agents based on observations of their physical behavior. These approaches are limited in that the relationship categories are modelled as independent and mutually exclusive, when in real world systems categories are often interacting. In this work, we introduce a level of abstraction between the physical behavior of agents and the categories that define their behavior. To do this, we learn a mapping from the agents' states to their affinities for each category in a graph neural network. We integrate the physical proximity of agents and their affinities in a nonlinear opinion dynamics model which provides a mechanism to identify mutually exclusive categories, predict an agent's evolution in time, and control an agent's behavior. We demonstrate the utility of our model for learning interpretable categories for mechanical systems, and demonstrate its efficacy on several long-horizon trajectory prediction benchmarks where we consistently out perform existing methods.

通过引入一个抽象层，本文利用图神经网络将代理的状态映射到它们对每个类别的关联度，将代理之间的物理接近性和关联度整合到非线性舆论动力学模型中，用于识别互斥类别、预测代理的时间演变和控制代理的行为，从而在机械系统的可学习类别和长期轨迹预测基准测试中证明了模型的实用性和效果优越性。

使用观点动力学在图上进行关系推理