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Found 23 papers, 1 papers with code
No-Regret Learning in Bilateral Trade via Global Budget Balance
Bilateral trade models the problem of intermediating between two rational agents -- a seller and a buyer -- both characterized by a private valuation for an item they want to trade.
Bandits with Replenishable Knapsacks: the Best of both Worlds
The bandits with knapsack (BwK) framework models online decision-making problems in which an agent makes a sequence of decisions subject to resource consumption constraints.
Online Learning under Budget and ROI Constraints via Weak Adaptivity
Finally, we show how to instantiate the framework to optimally bid in various mechanisms of practical relevance, such as first- and second-price auctions.
Fully Dynamic Online Selection through Online Contention Resolution Schemes
A successful approach to online selection problems in the adversarial setting is given by the notion of Online Contention Resolution Scheme (OCRS), that uses a priori information to formulate a linear relaxation of the underlying optimization problem, whose optimal fractional solution is rounded online for any adversarial order of the input sequence.
A Unifying Framework for Online Optimization with Long-Term Constraints
We present the first best-of-both-world type algorithm for this general class of problems, with no-regret guarantees both in the case in which rewards and constraints are selected according to an unknown stochastic model, and in the case in which they are selected at each round by an adversary.
Optimal Correlated Equilibria in General-Sum Extensive-Form Games: Fixed-Parameter Algorithms, Hardness, and Two-Sided Column-Generation
For team games, the two-sided column generation approach vastly outperforms standard column generation approaches, making it the state of the art algorithm when the parameter is large.
Best of Many Worlds Guarantees for Online Learning with Knapsacks
We study online learning problems in which a decision maker wants to maximize their expected reward without violating a finite set of $m$ resource constraints.
Multi-Receiver Online Bayesian Persuasion
Then, we focus on the case of submodular sender's utility functions and we show that, in this case, it is possible to design a polynomial-time no-$(1 - \frac{1}{e})$-regret algorithm.
Simple Uncoupled No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
The existence of simple uncoupled no-regret learning dynamics that converge to correlated equilibria in normal-form games is a celebrated result in the theory of multi-agent systems.
Multi-Agent Coordination in Adversarial Environments through Signal Mediated Strategies
Team members can coordinate their strategies before the beginning of the game, but are unable to communicate during the playing phase of the game.
Online Bayesian Persuasion
We are interested in no-regret algorithms prescribing a signaling scheme at each round of the repeated interaction with performances close to that of the best-in-hindsight signaling scheme.
Faster Algorithms for Optimal Ex-Ante Coordinated Collusive Strategies in Extensive-Form Zero-Sum Games
Second, we provide an algorithm that computes such an optimal distribution by only using profiles where only one of the team members gets to randomize in each profile.
No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
When each player has low trigger regret, the empirical frequency of play is close to an EFCE.
Signaling in Bayesian Network Congestion Games: the Subtle Power of Symmetry
A natural question is the following: is it possible for an informed sender to reduce the overall social cost via the strategic provision of information to players who update their beliefs rationally?
Public Bayesian Persuasion: Being Almost Optimal and Almost Persuasive
Unlike prior works on this problem, we study the public persuasion problem in the general setting with: (i) arbitrary state spaces; (ii) arbitrary action spaces; (iii) arbitrary sender's utility functions.
Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning
Many real-world applications involve teams of agents that have to coordinate their actions to reach a common goal against potential adversaries.
Learning to Correlate in Multi-Player General-Sum Sequential Games
In the context of multi-player, general-sum games, there is an increasing interest in solution concepts modeling some form of communication among players, since they can lead to socially better outcomes with respect to Nash equilibria, and may be reached through learning dynamics in a decentralized fashion.
Computer Science and Game Theory
Persuading Voters: It's Easy to Whisper, It's Hard to Speak Loud
In the former, we show that an optimal signaling scheme can be computed efficiently both under a $k$-voting rule and plurality voting.
Bayesian Persuasion with Sequential Games
After formalizing the notions of ex ante and ex interim persuasiveness (which differ in the time at which the receivers commit to following the sender's signaling scheme), we investigate the continuous optimization problem of computing a signaling scheme which maximizes the sender's expected revenue.
Computing Optimal Coarse Correlated Equilibria in Sequential Games
We investigate the computation of equilibria in extensive-form games where ex ante correlation is possible, focusing on correlated equilibria requiring the least amount of communication between the players and the mediator.
Ex ante coordination and collusion in zero-sum multi-player extensive-form games
This paper focuses on zero-sum games where a team of players faces an opponent, as is the case, for example, in Bridge, collusion in poker, and many non-recreational applications such as war, where the colluders do not have time or means of communicating during battle, collusion in bidding, where communication during the auction is illegal, and coordinated swindling in public.
Computational Results for Extensive-Form Adversarial Team Games
We provide, to the best of our knowledge, the first computational study of extensive-form adversarial team games.
Team-maxmin equilibrium: efficiency bounds and algorithms
The Team-maxmin equilibrium prescribes the optimal strategies for a team of rational players sharing the same goal and without the capability of correlating their strategies in strategic games against an adversary.
