Multiagent decision-making in partially observable environments is usually modelled as
either an extensive-form game (EFG) in game theory or a partially observable stochastic …

Develo** scalable solution algorithms is one of the central problems in computational
game theory. We present an iterative algorithm for computing an exact Nash equilibrium for …

Sparse iterative methods, in particular first-order methods, are known to be among the most
effective in solving large-scale two-player zero-sum extensive-form games. The …

Regret minimization is a powerful tool for solving large-scale extensive-form games. State-of-
the-art methods rely on minimizing regret locally at each decision point. In this work we …

We study the performance of optimistic regret-minimization algorithms for both minimizing
regret in, and computing Nash equilibria of, zero-sum extensive-form games. In order to …

Efficient algorithms exist for finding optimal policies in extensive-form games. However,
human-scale problems are typically so large that this computation remains infeasible with …

The leading approach for solving large imperfect-information games is automated
abstraction followed by running an equilibrium-finding algorithm. We introduce a distributed …

Recent milestones in equilibrium computation, such as the success of Libratus, show that it
is possible to compute strong solutions to two-player zero-sum games in theory and practice …

Most real-world games and many recreational games are games of incomplete information.
Over the last dozen years, abstraction has emerged as a key enabler for solving large …

Decision-making problems with two agents can be modeled as two player games, and a
Nash equilibrium is the basic solution concept describing good play in adversarial games …