论文标题
在紧急沟通中迈向图表学习
Towards Graph Representation Learning in Emergent Communication
论文作者
论文摘要
神经科学的最新发现表明，人脑在几何结构中表示信息（例如，通过概念空间）。为了进行交流，我们将实体的复杂表示及其属性变为单个单词或句子。在本文中，我们使用图形卷积网络来支持多代理系统中语言与合作的演变。在一个基于图像的参考游戏中，我们提出了一个具有不同程度复杂性的图形参考游戏，并且提供了强大的基线模型，这些模型以语言出现和合作的形式展现出理想的属性。我们表明，出现的通信协议是强大的，代理人发现了游戏中差异的真实因素，并且他们学会了概括训练期间遇到的样本之外的概括。
Recent findings in neuroscience suggest that the human brain represents information in a geometric structure (for instance, through conceptual spaces). In order to communicate, we flatten the complex representation of entities and their attributes into a single word or a sentence. In this paper we use graph convolutional networks to support the evolution of language and cooperation in multi-agent systems. Motivated by an image-based referential game, we propose a graph referential game with varying degrees of complexity, and we provide strong baseline models that exhibit desirable properties in terms of language emergence and cooperation. We show that the emerged communication protocol is robust, that the agents uncover the true factors of variation in the game, and that they learn to generalize beyond the samples encountered during training.
