As AI systems increasingly operate as autonomous agents, intelligence is becoming inherently social. Future AI systems will not act in isolation, but will learn, coordinate, communicate, and make decisions alongside humans and other agents in open-ended environments. This shift raises fundamental challenges: How can agents acquire cooperative behaviours? How can they adapt to human preferences and evolving social norms? And how can we ensure safety when intelligent agents interact at scale?
In this talk, we present a framework to ground cooperative AI in real-world human contexts. We will explore how to resolve the computational limits of complex social simulation, transition from hand-crafted rewards to dynamic value alignment via mixed-quality human feedback, and replace inflexible numerical costs with intuitive natural language safety boundaries. Together, these interconnected works outline a cohesive pathway for building cooperative AI that is highly scalable and deeply aligned with human intent. Finally, I will outline a research agenda for building trustworthy agent societies where learning, coordination and safety continue beyond deployment.
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