Open Problems in Cooperative AI (2012.08630v1)

Abstract: 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.

An Overview of "Open Problems in Cooperative AI"

The paper "Open Problems in Cooperative AI" authored by Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R. McKee, Joel Z. Leibo, Kate Larson, and Thore Graepel, addresses the importance and challenges of cooperation among intelligent agents, whether human or AI. The discourse on cooperative AI emphasizes the need for artificial intelligence research to tackle problems where agents can jointly enhance their welfare. These issues can range from common daily activities to significant global challenges.

Key Themes and Implications

The central thesis of the paper is the necessity for a dedicated research focus within AI to foster cooperative capabilities. As AI systems are increasingly integrated into various facets of human life, their ability to participate in and facilitate cooperative interactions is critical. The authors propose that the field of Cooperative AI should concentrate on developing machine agents equipped with social intelligence, capable of understanding, communicating, and acting upon cooperative opportunities.

The paper identifies several areas where Cooperative AI can draw insights, including multi-agent systems, game theory, and social choice. Each of these domains contributes to understanding the dynamics of cooperation, albeit from different angles and with varying emphases. The paper posits that Cooperative AI is not merely a conglomeration of these fields but represents a unique research trajectory focused on leveraging AI to solve cooperation problems.

Dimensions of Cooperative Opportunities

A critical aspect of the paper is the delineation of various dimensions of cooperative opportunities. This includes:

1. Common vs. Conflicting Interests: The nature of agent interests significantly affects cooperation dynamics. While pure common interest games offer the most straightforward opportunities for cooperation, mixed-motive scenarios are more common and pose intrinsic challenges. Games of pure conflicting interest, though less prevalent, showcase the limitations of cooperation.
2. Agent Type and Context: Different agents, whether humans, machines, or organizations, will have distinct cooperative dynamics. The nuances of these interactions demand tailored approaches within Cooperative AI research.
3. Individual vs. Planner Perspective: The paper distinguishes between focusing on enhancing individual agents' capabilities to cooperate and adopting a planner perspective to enhance social welfare through centralized interventions and institutions.
4. Scope and Relation to Adjacent Fields: Cooperative AI's scope involves clear boundaries with related areas such as human-machine interaction and AI alignment. It emphasizes horizontal coordination among multiple principals rather than the vertical alignment characteristic of AI safety.

Essential Capabilities

The authors outline key capabilities essential for Cooperative AI, including:

• Understanding: Agents must accurately predict the consequences of actions and the behavior of others to enable cooperation. This involves understanding others' strategies, preferences, and potentially recursive beliefs.
• Communication: Effective communication is vital for sharing information and coordinating actions. This can be straightforward in pure common interest scenarios but is more complex and challenging in mixed-motive situations.
• Commitment: Solving commitment problems, which inhibit effective cooperation even when information is complete and symmetric, is crucial. This involves developing mechanisms and structures that allow credible promises and threats.
• Institutions: Decentralized and centralized institutions facilitate coordination by providing rules and structures that guide cooperative behavior. These include norms, entitlements, and legal frameworks enhancing cooperation at scale.

Addressing Downsides

The paper does not shy away from addressing the potential downsides of Cooperative AI. It stresses the importance of being vigilant about how cooperative capabilities can inadvertently improve coercive capabilities or lead to exclusion and collusion. The inquiry into these downsides provides a balanced view and guides responsible research and policy-making.

Future Directions

The paper articulates a vision for AI research to contribute positively to global cooperation challenges. By advancing Cooperative AI, the field can play a pivotal role in solving some of humanity's most pressing issues, from resource management to conflict resolution. The authors call for a methodological expansion that bridges AI with social sciences, aiming to cultivate a shared understanding and novel solutions for cooperative dynamics.

In conclusion, this paper underscores the essential role of Cooperative AI research in navigating the complexities of agent interaction and coordination. It highlights significant opportunities for theoretical and practical advances, fostering a future where AI systems enhance, rather than complicate, human cooperation. This scholarly conversation is expected to catalyze developments in AI that align with societal progress and ethical integrity.