Cooperative AI

Natasha Jaques · Edward Hughes · Jakob Foerster · Noam Brown · Kalesha Bullard · Charlotte Smith

Abstract Workshop Website
Tue 14 Dec, 5:20 a.m. PST


The human ability to cooperate in a wide range of contexts is a key ingredient in the success of our species. Problems of cooperation—in which agents seek ways to jointly improve their welfare—are ubiquitous and important. They can be found at every scale, from the daily routines of highway driving, communicating in shared language and work collaborations, to the global challenges of climate change, pandemic preparedness and international trade. With AI agents playing an ever greater role in our lives, we must endow them with similar abilities. In particular they must understand the behaviors of others, find common ground by which to communicate with them, make credible commitments, and establish institutions which promote cooperative behavior. By construction, the goal of Cooperative AI is interdisciplinary in nature. Therefore, our workshop will bring together scholars from diverse backgrounds including reinforcement learning (and inverse RL), multi-agent systems, human-AI interaction, game theory, mechanism design, social choice, fairness, cognitive science, language learning, and interpretability. This year we will organize the workshop along two axes. First, we will discuss how to incentivize cooperation in AI systems, developing algorithms that can act effectively in general-sum settings, and which encourage others to cooperate. The second focus is on how to implement effective coordination, given that cooperation is already incentivized. For example, we may examine zero-shot coordination, in which AI agents need to coordinate with novel partners at test time. This setting is highly relevant to human-AI coordination, and provides a stepping stone for the community towards full Cooperative AI.

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