Emergent Language-Based Coordination In Deep Multi-Agent Systems

Abstract

Large pre-trained deep networks are the standard building blocks of modern AI applications.This raises fundamental questions about how to control their behaviour and how to make them efficiently interact with each other.Deep net emergent communication tackles these challenges by studying how to induce communication protocols between neural network agents, and how to include humans in the communication loop.Traditionally, this research had focussed on relatively small-scale experiments where two networks had to develop a discrete code from scratch for referential communication.However, with the rise of large pre-trained language models that can work well on many tasks, the emphasis is now shifting on how to let these models interact through a language-like channel to engage in more complex behaviors.By reviewing several representative papers, we will provide an introduction to deep net emergent communication, we will cover various central topics from the present and recent past, as well as discussing current shortcomings and suggest future directions.The presentation is complemented by a hands-on section where participants will implement and analyze two emergent communications setups from the literature.The tutorial should be of interest to researchers wanting to develop more flexible AI systems, but also to cognitive scientists and linguists interested in the evolution of communication systems.

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