The ability to cooperate through language is a defining feature of humans. As the
perceptual, motory and planning capabilities of deep artificial networks increase …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

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 …

We propose a unified mechanism for achieving coordination and communication in Multi-
Agent Reinforcement Learning (MARL), through rewarding agents for having causal …

Abstract We introduce the Neural State Machine, seeking to bridge the gap between the
neural and symbolic views of AI and integrate their complementary strengths for the task of …

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 …

Emergent communication aims for a better understanding of human language evolution and
building more efficient representations. We posit that reaching these goals will require …

Many machine learning algorithms represent input data with vector embeddings or discrete
codes. When inputs exhibit compositional structure (eg objects built from parts or procedures …

How do we know if communication is emerging in a multi-agent system? The vast majority of
recent papers on emergent communication show that adding a communication channel …

Communication requires having a common language, a lingua franca, between agents. This
language could emerge via a consensus process, but it may require many generations of …