Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …

Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …

Research into society's informal rules of conduct, or norms, has recently experienced a
surge, extending across multiple academic disciplines. Despite this growth, the theoretical …

Multi-agent reinforcement learning (MARL) is a powerful tool for training automated systems
acting independently in a common environment. However, it can lead to sub-optimal …

Abstract Multi-agent Reinforcement Learning (MARL) is a powerful tool for training
autonomous agents acting independently in a common environment. However, it can lead to …

What is appropriateness? Humans navigate a multi-scale mosaic of interlocking notions of
what is appropriate for different situations. We act one way with our friends, another with our …

Human ecological success relies on our characteristic ability to flexibly self-organize into
cooperative social groups, the most successful of which employ substantial specialization …

Altruistic cooperation is costly yet socially desirable. As a result, agents struggle to learn
cooperative policies through independent reinforcement learning (RL). Indirect reciprocity …

Solving the problem of cooperation is of fundamental importance to the creation and
maintenance of functional societies, with examples of cooperative dilemmas ranging from …

In multi-agent reinforcement learning, each agent acts to maximize its individual
accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect …