Abstract: https://www.CooperativeAI.com/

Problems of cooperation—in which agents seek ways to jointly improve their welfare—are ubiquitous and important. They can be found at all scales ranging from our daily routines—such as highway driving, communication via shared language, division of labor, and work collaborations—to our global challenges—such as disarmament, climate change, global commerce, and pandemic preparedness. Arguably, the success of the human species is rooted in our ability to cooperate, in our social intelligence and skills. Since machines powered by artificial intelligence and machine learning are playing an ever greater role in our lives, it will be important to equip them with the skills necessary to cooperate and to foster cooperation.

We see an opportunity for the field of AI, and particularly machine learning, to explicitly focus effort on this class of problems which we term Cooperative AI. The goal of this research would be to study the many aspects of the problem of cooperation, and innovate in AI to contribute to solving these problems. Central questions include how to build machine agents with the capabilities needed for cooperation, and how advances in machine learning can help foster cooperation in populations of agents (of machines and/or humans), such as through improved mechanism design and mediation.

Research could be organized around key capabilities necessary for cooperation, including: understanding other agents, communicating with other agents, constructing cooperative commitments, and devising and negotiating suitable bargains and institutions. Since artificial agents will often act on behalf of particular humans and in ways that are consequential for humans, this research will need to consider how machines can adequately learn human preferences, and how best to integrate human norms and ethics into cooperative arrangements.

We are planning to bring together scholars from diverse backgrounds to discuss how AI research can contribute to the field of cooperation.


Call for Papers
We invite high-quality paper submissions on the following topics (broadly construed, this is not an exhaustive list):

-Multi-agent learning
-Agent cooperation
-Agent communication
-Resolving commitment problems
-Agent societies, organizations and institutions
-Trust and reputation
-Theory of mind and peer modelling
-Markets, mechanism design and and economics based cooperation
-Negotiation and bargaining agents
-Team formation problems

Accepted papers will be presented during joint virtual poster sessions and be made publicly available as non archival reports, allowing future submissions to archival conferences or journals.

Submissions should be up to eight pages excluding references, acknowledgements, and supplementary material, and should follow NeurIPS format. The review process will be double-blind.

Paper submissions: https://easychair.org/my/conference?conf=coopai2020#