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.

本文研究了如何平衡效用、信息的有效性和复杂性，从而塑造新兴的通信，并将其与人类通信进行比较。我们使用 VQ-VIB 方法训练神经代理来压缩输入以获得离散信号，从而比以往的神经体系结构在其性能方面更具优越性，并发现惩罚交际复杂性可以维持高效用的同时维持类似人类的词汇量。此外，我们还发现，VQ-VIB 优于其他离散通信方法。

基于信息瓶颈原理的人 - 代理通信

Towards Human-Agent Communication via the Information Bottleneck Principle

The ability to cooperate through language is a defining feature of humans. As
the perceptual, motory and planning capabilities of deep artificial networks
increase, researchers are studying whether they also can develop a shared
language to interact. From a scientific perspective, understanding the
conditions under which language evolves in communities of deep agents and its
emergent features can shed light on human language evolution. From an applied
perspective, endowing deep networks with the ability to solve problems
interactively by communicating with each other and with us should make them
more flexible and useful in everyday life.
This article surveys representative recent language emergence studies from
both of these two angles.

研究表明，随着深度人工网络的感知、移动和规划能力的增强，研究人员正在研究它们是否也能发展出共享语言来相互交互，并探讨语言在深度代理人群体中演化及紧急特性的条件，这既有助于理解人类语言进化的过程，也可以让深度网络在日常生活中更加灵活和有用。本文概述了各角度最具代表性的语言演化研究。