Understanding the emergence of symbol systems, especially language, requires the
construction of a computational model that reproduces both the developmental learning …

Generalization remains a central challenge in machine learning. In this work, we propose
Learning from Teaching (LoT), a novel regularization technique for deep neural networks to …

Rapid progress in machine learning for natural language processing has the potential to
transform debates about how humans learn language. However, the learning environments …

Lewis signaling games are a class of simple communication games for simulating the
emergence of language. In these games, two agents must agree on a communication …

Emergent communication, or emergent language, is the field of research which studies how
human language-like communication systems emerge de novo in deep multi-agent …

Constructive studies on symbol emergence systems seek to investigate computational
models that can better explain human language evolution, the creation of symbol systems …

Communication is a prerequisite for collaboration. When scaling networks of AI-powered
agents, communication must be versatile, efficient, and portable. These requisites, which we …

Vision-based emergent communication (EC) aims to learn to communicate through sketches
and demystify the evolution of human communication. Ironically, previous works neglect …

Emergent communication protocols among humans and artificial neural network agents do
not yet share the same properties and show some critical mismatches in results. We …

In the studies on symbol emergence and emergent communication in a population of
agents, a computational model was employed in which agents participate in various …