论文标题
通过信息瓶颈原则进行人类代理通信
Towards Human-Agent Communication via the Information Bottleneck Principle
论文作者
论文摘要
新兴的沟通研究通常着重于优化特定于任务的效用作为交流的驱动力。但是，通过优化信息性和复杂性之间的信息瓶颈权衡，人类语言似乎在压力下发展，以有效地将含义压缩到通信信号中。在这项工作中，我们研究了如何交换这三个因素：效用，信息性和复杂性 - 与人类交流相比，包括新兴的沟通。为此，我们提出了矢量定量的变分信息瓶颈（VQ-VIB），这是一种训练神经剂将输入压缩到嵌入连续空间中的离散信号的方法。我们通过VQ-VIB训练代理商，并将其性能与以前建议的神经体系结构在接地环境和刘易斯参考游戏中进行了比较。在所有神经体系结构和环境中，考虑到交流信息有益的沟通融合率，并惩罚交流复杂性会导致类似人类的词典大小，同时保持高效用。此外，我们发现VQ-VIB优于其他离散通信方法。这项工作表明，人们认为人类语言进化的基本原理如何为人工代理中的新兴沟通提供信息。
Emergent communication research often focuses on optimizing task-specific utility as a driver for communication. However, human languages appear to evolve under pressure to efficiently compress meanings into communication signals by optimizing the Information Bottleneck tradeoff between informativeness and complexity. In this work, we study how trading off these three factors -- utility, informativeness, and complexity -- shapes emergent communication, including compared to human communication. To this end, we propose Vector-Quantized Variational Information Bottleneck (VQ-VIB), a method for training neural agents to compress inputs into discrete signals embedded in a continuous space. We train agents via VQ-VIB and compare their performance to previously proposed neural architectures in grounded environments and in a Lewis reference game. Across all neural architectures and settings, taking into account communicative informativeness benefits communication convergence rates, and penalizing communicative complexity leads to human-like lexicon sizes while maintaining high utility. Additionally, we find that VQ-VIB outperforms other discrete communication methods. This work demonstrates how fundamental principles that are believed to characterize human language evolution may inform emergent communication in artificial agents.
