The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the
world around them and taking actions to advance specified goals. Put another way, AI …

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 …

Multiagent systems combine multiple autonomous entities, each having diverging interests
or different information. This overview of the field offers a computer science perspective, but …

We study the problem of implementing equilibria of complete information games in settings
of incomplete information, and address this problem using" recommender mechanisms." A …

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 …

For common notions of correlated equilibrium in extensive-form games, computing an
optimal (eg, welfare-maximizing) equilibrium is NP-hard. Other equilibrium notions …

We study a setting of non-atomic routing in a network of m parallel links with asymmetry of
information. While a central entity (such as a GPS navigation system)—a mediator hereafter …

The transition of the advertising market from traditional media to the internet has induced a
proliferation of marketing agencies specialized in bidding in the auctions that are used to …

The majority of Multi-Agent Reinforcement Learning (MARL) literature equates the
cooperation of self-interested agents in mixed environments to the problem of social welfare …

We study a very general class of games—multi-dimensional aggregative games—which in
particular generalize both anonymous games and weighted congestion games. For any …