no code implementations • 18 Nov 2016 • Nicola Basilico, Andrea Celli, Giuseppe De Nittis, Nicola Gatti
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.
no code implementations • 18 Nov 2017 • Andrea Celli, Nicola Gatti
We provide, to the best of our knowledge, the first computational study of extensive-form adversarial team games.
no code implementations • NeurIPS 2018 • Gabriele Farina, Andrea Celli, Nicola Gatti, Tuomas Sandholm
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.
no code implementations • 18 Jan 2019 • Andrea Celli, Stefano Coniglio, Nicola Gatti
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.
no code implementations • 2 Aug 2019 • Andrea Celli, Stefano Coniglio, Nicola Gatti
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.
no code implementations • 28 Aug 2019 • Matteo Castiglioni, Andrea Celli, Nicola Gatti
In the former, we show that an optimal signaling scheme can be computed efficiently both under a $k$-voting rule and plurality voting.
1 code implementation • NeurIPS 2019 • Andrea Celli, Alberto Marchesi, Tommaso Bianchi, Nicola Gatti
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
no code implementations • 16 Dec 2019 • Andrea Celli, Marco Ciccone, Raffaele Bongo, Nicola Gatti
Many real-world applications involve teams of agents that have to coordinate their actions to reach a common goal against potential adversaries.
no code implementations • 12 Feb 2020 • Matteo Castiglioni, Andrea Celli, Nicola Gatti
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.
no code implementations • 12 Feb 2020 • Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti
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?
no code implementations • NeurIPS 2020 • Andrea Celli, Alberto Marchesi, Gabriele Farina, Nicola Gatti
When each player has low trigger regret, the empirical frequency of play is close to an EFCE.
no code implementations • 21 Sep 2020 • Gabriele Farina, Andrea Celli, Nicola Gatti, Tuomas Sandholm
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 code implementations • NeurIPS 2020 • Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti
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.
no code implementations • 9 Feb 2021 • Federico Cacciamani, Andrea Celli, Marco Ciccone, Nicola Gatti
Team members can coordinate their strategies before the beginning of the game, but are unable to communicate during the playing phase of the game.
no code implementations • 4 Apr 2021 • Gabriele Farina, Andrea Celli, Alberto Marchesi, Nicola Gatti
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.
no code implementations • 11 Jun 2021 • Matteo Castiglioni, Alberto Marchesi, Andrea Celli, Nicola Gatti
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.
no code implementations • 28 Feb 2022 • Andrea Celli, Matteo Castiglioni, Christian Kroer
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.
no code implementations • 14 Mar 2022 • Brian Zhang, Gabriele Farina, Andrea Celli, Tuomas Sandholm
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.
no code implementations • 15 Sep 2022 • Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Giulia Romano, Nicola Gatti
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.
no code implementations • 8 Jan 2023 • Vashist Avadhanula, Andrea Celli, Riccardo Colini-Baldeschi, Stefano Leonardi, Matteo Russo
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.
no code implementations • 2 Feb 2023 • Matteo Castiglioni, Andrea Celli, Christian Kroer
Finally, we show how to instantiate the framework to optimally bid in various mechanisms of practical relevance, such as first- and second-price auctions.
no code implementations • 14 Jun 2023 • Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Federico Fusco
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.
no code implementations • 18 Oct 2023 • Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Federico Fusco
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.