Reasoning about objects, relations, and physics is central to human
intelligence, and a key goal of artificial intelligence. Here we introduce the
interaction network, a model which can reason about how objects in complex
systems interact, supporting dynamical predictions, as well as inferences about
the abstract properties of the system. Our model takes graphs as input,
performs object- and relation-centric reasoning in a way that is analogous to a
simulation, and is implemented using deep neural networks. We evaluate its
ability to reason about several challenging physical domains: n-body problems,
rigid-body collision, and non-rigid dynamics. Our results show it can be
trained to accurately simulate the physical trajectories of dozens of objects
over thousands of time steps, estimate abstract quantities such as energy, and
generalize automatically to systems with different numbers and configurations
of objects and relations. Our interaction network implementation is the first
general-purpose, learnable physics engine, and a powerful general framework for
reasoning about object and relations in a wide variety of complex real-world
domains.

本文介绍了交互网络模型，它能够推断复杂系统中物体之间相互作用的方式，支持对系统动态预测，以及对系统抽象属性的推理。该模型使用深度神经网络进行对象和关系居中的推理，并可以推理多种具有挑战性的物理领域。 报告结果表明，它能够训练准确地模拟数十个对象在数千个时间步骤上的物理轨迹，估计能量等抽象量，自动推广到具有不同数量和配置的对象和关系的系统中。 该交互网络的实现是第一个通用的，可学习的物理引擎，是处理各种复杂真实世界领域中物体和关系的强大的通用框架。