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To verify the robustness of a program or protocol, it is common in the computer science community to rely on the theoretical framework of game theory. In particular, if one seeks to enforce a desired property, or specification, despite an…

Computer Science and Game Theory · Computer Science 2026-05-20 Léonard Brice

Game Theory concepts have been successfully applied in a wide variety of domains over the past decade. Sports and games are one of the popular areas of game theory application owing to its merits and benefits in solving complex scenarios.…

Computer Science and Game Theory · Computer Science 2021-05-26 Ambareesh Ravi , Atharva Gokhale , Anchit Nagwekar

Zero-sum games have long guided artificial intelligence research, since they possess both a rich strategy space of best-responses and a clear evaluation metric. What's more, competition is a vital mechanism in many real-world multi-agent…

Computer Science and Game Theory · Computer Science 2020-03-03 Edward Hughes , Thomas W. Anthony , Tom Eccles , Joel Z. Leibo , David Balduzzi , Yoram Bachrach

This paper introduces a systematic methodological framework to design and analyze distributed algorithms for optimization and games over networks. Starting from a centralized method, we identify an aggregation function involving all the…

Optimization and Control · Mathematics 2025-05-26 Guido Carnevale , Nicola Mimmo , Giuseppe Notarstefano

Constrained submodular set function maximization problems often appear in multi-agent decision-making problems with a discrete feasible set. A prominent example is the problem of multi-agent mobile sensor placement over a discrete domain.…

Optimization and Control · Mathematics 2021-08-02 Navid Rezazadeh , Solmaz S. Kia

We introduce and study the problem of planning a trajectory for an agent to carry out a scouting mission while avoiding being detected by an adversarial guard. This introduces an adversarial version of classical visibility-based planning…

Robotics · Computer Science 2019-02-26 Zhongshun Zhang , Yoonchang Sung , Lifeng Zhou , Jonathon M. Smereka , Joseph Lee , Pratap Tokekar

We present a deterministic algorithm, solving discounted games with $n$ nodes in $n^{O(1)}\cdot (2 + \sqrt{2})^n$-time. For bipartite discounted games our algorithm runs in $n^{O(1)}\cdot 2^n$-time. Prior to our work no deterministic…

Data Structures and Algorithms · Computer Science 2020-10-27 Alexander Kozachinskiy

Despite the many recent practical and theoretical breakthroughs in computational game theory, equilibrium finding in extensive-form team games remains a significant challenge. While NP-hard in the worst case, there are provably efficient…

Computer Science and Game Theory · Computer Science 2022-01-19 Brian Hu Zhang , Tuomas Sandholm

Recent successes of game-theoretic formulations in ML have caused a resurgence of research interest in differentiable games. Overwhelmingly, that research focuses on methods and upper bounds on their speed of convergence. In this work, we…

Machine Learning · Computer Science 2020-09-16 Adam Ibrahim , Waïss Azizian , Gauthier Gidel , Ioannis Mitliagkas

In this paper, we settle the sampling complexity of solving discounted two-player turn-based zero-sum stochastic games up to polylogarithmic factors. Given a stochastic game with discount factor $\gamma\in(0,1)$ we provide an algorithm that…

Machine Learning · Computer Science 2019-08-30 Aaron Sidford , Mengdi Wang , Lin F. Yang , Yinyu Ye

The Boolean matrix factorization problem consists in approximating a matrix by the Boolean product of two smaller Boolean matrices. To obtain optimal solutions when the matrices to be factorized are small, we propose SAT and MaxSAT…

Machine Learning · Computer Science 2021-06-21 Florent Avellaneda , Roger Villemaire

This paper presents a new lower bound for the discrete strategy improvement algorithm for solving parity games due to Voege and Jurdziski. First, we informally show which structures are difficult to solve for the algorithm. Second, we…

Computer Science and Game Theory · Computer Science 2009-01-20 Oliver Friedmann

A polyhedral convex set optimization problem is given by a set-valued objective mapping from the $n$-dimensional to the $q$-dimensional Euclidean space whose graph is a convex polyhedron. This problem can be seen as the most elementary…

Optimization and Control · Mathematics 2023-04-25 Niklas Hey , Andreas Löhne

Nash equilibria and Pareto optimality are two distinct concepts when dealing with multiple criteria. It is well known that the two concepts do not coincide. However, in this work we show that it is possible to characterize the set of all…

Optimization and Control · Mathematics 2022-12-12 Zachary Feinstein , Birgit Rudloff

The connection between game theory, convex optimization, and geometry is deep. There are many applications of linear programming methods and polyhedral representation conversion methods in game theory. In this paper, we discuss two more…

Computer Science and Game Theory · Computer Science 2025-09-03 Zhuoer Zhang , Bryce Morsky

We refine existing general network optimization techniques, give new characterizations for the class of problems to which they can be applied, and show that they can also be used to solve various two-player games in almost linear time.…

Computer Science and Game Theory · Computer Science 2008-01-29 Daniel Andersson

We study the conditions under which the iterated elimination of strictly dominated strategies is order independent and we identify a class of discontinuous games for which order does not matter. In this way, we answer the open problem…

Optimization and Control · Mathematics 2013-03-29 Monica Patriche

Dynamic zero-sum games are an important class of problems with applications ranging from evasion-pursuit and heads-up poker to certain adversarial versions of control problems such as multi-armed bandit and multiclass queuing problems.…

Computer Science and Game Theory · Computer Science 2015-06-12 Martin Haugh , Chun Wang

In this paper, we consider zero-sum repeated games in which the maximizer is restricted to strategies requiring no more than a limited amount of randomness. Particularly, we analyze the maxmin payoff of the maximizer in two models: the…

Information Theory · Computer Science 2018-10-11 Mehrdad Valizadeh , Amin Gohari

In an instance of the minimum eigenvalue problem, we are given a collection of $n$ vectors $v_1,\ldots, v_n \subset {\mathbb{R}^d}$, and the goal is to pick a subset $B\subseteq [n]$ of given vectors to maximize the minimum eigenvalue of…

Data Structures and Algorithms · Computer Science 2024-01-26 Adam Brown , Aditi Laddha , Mohit Singh