Towards Human-Agent Communication via the Information Bottleneck Principle

Understand

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.