In coming decades, artificial general intelligence (AGI) may surpass human capabilities at
many critical tasks. We argue that, without substantial effort to prevent it, AGIs could learn to …

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 …

AI systems can interact in unexpected ways, sometimes with disastrous consequences. As
AI gets to control more of our world, these interactions will become more common and have …

Framed in positive terms, this report examines how technical AI research might be steered in
a manner that is more attentive to humanity's long-term prospects for survival as a species …

Game-theoretic interactions with AI agents could differ from traditional human-human
interactions in various ways. One such difference is that it may be possible to simulate an AI …

It is increasingly possible for real-world agents, such as software-based agents or human
institutions, to view the internal programming of other such agents that they interact with. For …

As machine learning agents act more autonomously in the world, they will increasingly
interact with each other. Unfortunately, in many social dilemmas like the one-shot Prisoner's …

The Center on Long-Term Risk's research agenda on Cooperation, Conflict, and
Transformative Artificial Intelligence outlines what we think are the most promising avenues …

As machine learning agents act more autonomously in the world, they will increasingly
interact with each other. Unfortunately, in many social dilemmas like the one-shot Prisoner's …

In reinforcement learning (RL), adversarial policies can be developed by training an
adversarial agent to minimize a target agent's rewards. Prior work has studied black-box …