  Problems of cooperation--in which agents seek ways to jointly improve their
welfare--are ubiquitous and important. They can be found at scales ranging from
our daily routines--such as driving on highways, scheduling meetings, and
working collaboratively--to our global challenges--such as peace, commerce, and
pandemic preparedness. Arguably, the success of the human species is rooted in
our ability to cooperate. Since machines powered by artificial intelligence are
playing an ever greater role in our lives, it will be important to equip them
with the capabilities necessary to cooperate and to foster cooperation.
  We see an opportunity for the field of artificial intelligence to explicitly
focus effort on this class of problems, which we term Cooperative AI. The
objective of this research would be to study the many aspects of the problems
of cooperation and to innovate in AI to contribute to solving these problems.
Central goals include building machine agents with the capabilities needed for
cooperation, building tools to foster cooperation in populations of (machine
and/or human) agents, and otherwise conducting AI research for insight relevant
to problems of cooperation. This research integrates ongoing work on
multi-agent systems, game theory and social choice, human-machine interaction
and alignment, natural-language processing, and the construction of social
tools and platforms. However, Cooperative AI is not the union of these existing
areas, but rather an independent bet about the productivity of specific kinds
of conversations that involve these and other areas. We see opportunity to more
explicitly focus on the problem of cooperation, to construct unified theory and
vocabulary, and to build bridges with adjacent communities working on
cooperation, including in the natural, social, and behavioural sciences.
