Nash equilibrium is one of the most influential solution concepts in game theory. With the
development of computer science and artificial intelligence, there is an increasing demand …

We introduce a new approach for computing optimal equilibria via learning in games. It
applies to extensive-form settings with any number of players, including mechanism design …

Normal-form games (NFGs) are the fundamental model of strategic interaction. We study
their representation using neural networks. We describe the inherent equivariance of NFGs …

Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an
evader in graph-based environments such as urban street networks. Recent advancements …

Recently, remarkable progress has been made by approximating Nash equilibrium (NE),
correlated equilibrium (CE), and coarse correlated equilibrium (CCE) through function …

A fundamental shortcoming of the concept of Nash equilibrium is its computational
intractability: approximating Nash equilibria in normal-form games is PPAD-hard. In this …

Auction games have been widely used in plenty of trading environments such as online
advertising and real estate. The complexity of real-world scenarios, characterized by diverse …

We study computationally efficient methods for finding equilibria in n-player general-sum
games, specifically ones that afford complex visuomotor skills. We show how existing …

In this paper, we investigate the learnability of the function approximator that approximates
Nash equilibrium (NE) for games generated from a distribution. First, we offer a …

Strategic interactions can be represented more concisely, and analyzed and solved more
efficiently, if we are aware of the symmetries within the multiagent system. Symmetries also …