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

Communication is an effective mechanism for coordinating the behaviors of multiple agents,
broadening their views of the environment, and to support their collaborations. In the field of …

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 …

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

In the recent shift towards human-centric AI, the need for machines to accurately use natural
language has become increasingly important. While a common approach to achieve this is …

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 …

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

In different wireless network scenarios, multiple network entities need to cooperate in order
to achieve a common task with minimum delay and energy consumption. Future wireless …

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 …

Abstract Multi-Agent Reinforcement Learning (MARL) methods have shown promise in
enabling agents to learn a shared communication protocol from scratch and accomplish …