no code implementations • 6 Mar 2024 • Francesco Emanuele Stradi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
To the best of our knowledge, our work is the first to study CMDPs involving both adversarial losses and hard constraints.
no code implementations • 5 Feb 2024 • Francesco Bacchiocchi, Francesco Emanuele Stradi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
Recently, Markov persuasion processes (MPPs) have been introduced to capture sequential scenarios where a sender faces a stream of myopic receivers in a Markovian environment.
no code implementations • 4 Oct 2023 • Gianmarco Genalti, Lupo Marsigli, Nicola Gatti, Alberto Maria Metelli
In this setting, we study the regret minimization problem when $\epsilon$ and $u$ are unknown to the learner and it has to adapt.
no code implementations • 18 Sep 2023 • Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
We design an algorithm that learns an approximately-optimal contract with high probability in a number of rounds polynomial in the size of the outcome space, when the number of actions is constant.
no code implementations • 27 Apr 2023 • Jacopo Germano, Francesco Emanuele Stradi, Gianmarco Genalti, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
We study online learning in episodic constrained Markov decision processes (CMDPs), where the goal of the learner is to collect as much reward as possible over the episodes, while guaranteeing that some long-term constraints are satisfied during the learning process.
1 code implementation • 12 Dec 2022 • Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran, Marco Mussi, Marcello Restelli, Nicola Gatti, Alberto Maria Metelli
Autoregressive processes naturally arise in a large variety of real-world scenarios, including stock markets, sales forecasting, weather prediction, advertising, and pricing.
no code implementations • 17 Nov 2022 • Marco Mussi, Gianmarco Genalti, Alessandro Nuara, Francesco Trovò, Marcello Restelli, Nicola Gatti
We ran a real-world 4-month-long A/B testing experiment in collaboration with an Italian e-commerce company, in which our algorithm PVD-B-corresponding to A configuration-has been compared with human pricing specialists-corresponding to B configuration.
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 Sep 2022 • Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti, Francesco Trovo
At each round, the sender observes the realizations of random events in the SDM problem.
no code implementations • 18 Jun 2022 • Luca Carminati, Federico Cacciamani, Marco Ciccone, Nicola Gatti
This work shows that we can recover from this weakness by bridging the gap between sequential adversarial team games and 2-player games.
no code implementations • 1 Jun 2022 • Giulia Romano, Andrea Agostini, Francesco Trovò, Nicola Gatti, Marcello Restelli
We provide two algorithms to address TP-MAB problems, namely, TP-UCB-FR and TP-UCB-EW, which exploit the partial information disclosed by the reward collected over time.
no code implementations • 25 Jan 2022 • Luca Carminati, Federico Cacciamani, Marco Ciccone, Nicola Gatti
Interestingly, we show that our game is more expressive than the original extensive-form game as any state/action abstraction of the extensive-form game can be captured by our representation, while the reverse does not hold.
no code implementations • 18 Jan 2022 • Matteo Castiglioni, Alessandro Nuara, Giulia Romano, Giorgio Spadaro, Francesco Trovò, Nicola Gatti
More interestingly, we provide an algorithm, namely GCB_{safe}(\psi,\phi), guaranteeing both sublinear pseudo-regret and safety w. h. p.
no code implementations • NeurIPS 2021 • Martino Bernasconi-de-Luca, Federico Cacciamani, Simone Fioravanti, Nicola Gatti, Alberto Marchesi, Francesco Trovò
Recently, game-playing agents based on AI techniques have demonstrated super-human performance in several sequential games, such as chess, Go, and poker.
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 • 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 • 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 • 11 Dec 2020 • Alberto Marchesi, Nicola Gatti
After providing an axiomatic definition of EFPCE, we show that one always exists since any perfect (Nash) equilibrium constitutes an EFPCE, and that it is a refinement of EFCE, as any EFPCE is also an EFCE.
Computer Science and Game Theory
no code implementations • 10 Dec 2020 • Giulia Romano, Gianluca Tartaglia, Alberto Marchesi, Nicola Gatti
We evaluate our mechanisms in terms of competitive ratio, measuring the worst-case ratio between their revenue and that of an optimal mechanism that knows the distribution of valuations.
Computer Science and Game Theory
no code implementations • 9 Dec 2020 • Matteo Castiglioni, Nicola Gatti
We study both private signaling, in which the sender can use a private communication channel per receiver, and public signaling, in which the sender can use a single communication channel for all the receivers.
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 • 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 • 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 • 3 Mar 2020 • Alessandro Nuara, Francesco Trovò, Nicola Gatti, Marcello Restelli
We experimentally evaluate our algorithms with synthetic settings generated from real data from Yahoo!, and we present the results of the adoption of our algorithms in a real-world application with a daily average spent of 1, 000 Euros for more than one year.
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 • 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 • 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 • 18 Nov 2019 • Alberto Marchesi, Francesco Trovò, Nicola Gatti
As a result, solving these games begets the challenge of designing learning algorithms that can find (approximate) equilibria with high confidence, using as few simulator queries as possible.
no code implementations • 14 Nov 2019 • Matteo Castiglioni, Nicola Gatti, Giulia Landriani, Diodato Ferraioli
We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election.
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 • 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.
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 • 11 Feb 2019 • Matteo Castiglioni, Diodato Ferraioli, Giulia Landriani, Nicola Gatti
We study the problem of election control through social influence when the manipulator is allowed to use the locations that she acquired on the network for sending \emph{both} positive and negative messages on \emph{multiple} candidates, widely extending the previous results available in the literature that study the influence of a single message on a single candidate.
Computer Science and Game Theory
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 • NeurIPS 2018 • Gabriele Farina, Nicola Gatti, Tuomas Sandholm
Nash equilibrium strategies have the known weakness that they do not prescribe rational play in situations that are reached with zero probability according to the strategies themselves, for example, if players have made mistakes.
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 • 31 Jul 2018 • Giuseppe De Nittis, Alberto Marchesi, Nicola Gatti
We study the computational complexity of finding or approximating an optimistic or pessimistic leader-follower equilibrium in specific classes of succinct games---polymatrix like---which are equivalent to 2-player Bayesian games with uncertainty over the follower, with interdependent or independent types.
no code implementations • 19 Jun 2018 • Giuseppe De Nittis, Nicola Gatti
This survey presents the main results achieved for the influence maximization problem in social networks.
no code implementations • 19 Jun 2018 • Giuseppe De Nittis, Nicola Gatti
We show that even the error of just a single resource can lead to an arbitrary inefficiency, when the inefficiency is defined as the ratio of the Defender's utilities obtained with a wrong guess and a correct guess.
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 • 7 Jul 2017 • Nicola Basilico, Stefano Coniglio, Nicola Gatti
The concept of leader--follower (or Stackelberg) equilibrium plays a central role in a number of real--world applications of game theory.
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 • 17 Nov 2016 • Stefano Paladino, Francesco Trovò, Marcello Restelli, Nicola Gatti
We study, to the best of our knowledge, the first Bayesian algorithm for unimodal Multi-Armed Bandit (MAB) problems with graph structure.
no code implementations • 7 Jun 2016 • Nicola Basilico, Giuseppe De Nittis, Nicola Gatti
The central problem with an alarm system, unexplored in other Security Games, is finding the best strategy to respond to alarm signals for each mobile defensive resource.
no code implementations • 9 Jun 2015 • Nicola Basilico, Giuseppe De Nittis, Nicola Gatti
That is, the alarm system is able to detect an attack but it is uncertain on the exact position where the attack is taking place.