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Found 15 papers, 1 papers with code
Estimation Network Design framework for efficient distributed optimization
Distributed decision problems features a group of agents that can only communicate over a peer-to-peer network, without a central memory.
An Efficient Risk-aware Branch MPC for Automated Driving that is Robust to Uncertain Vehicle Behaviors
One of the critical challenges in automated driving is ensuring safety of automated vehicles despite the unknown behavior of the other vehicles.
Automated Lane Merging via Game Theory and Branch Model Predictive Control
To overcome the challenge of multi-modal driving behavior exhibited by the surrounding vehicles, we formulate the trajectory selection as a matrix game and compute some equilibrium solutions.
A Semi-Decentralized Tikhonov-based Algorithm for Optimal Generalized Nash Equilibrium Selection
To optimally select a generalized Nash equilibrium, in this paper, we propose a semi-decentralized algorithm based on a double-layer Tikhonov regularization method.
Probabilistic Game-Theoretic Traffic Routing
We examine the routing problem for self-interested vehicles using stochastic decision strategies.
Online coalitional games for real-time payoff distribution with applications to energy markets
Motivated by the markets operating on fast time scales, we present a framework for online coalitional games with time-varying coalitional values and propose real-time payoff distribution mechanisms.
A Market for Trading Forecasts: A Wagering Mechanism
This market delivers an aggregated forecast to the buyer (pre-event) and allocates a payoff to the sellers (post-event) for their contribution.
Optimal selection and tracking of generalized Nash equilibria in monotone games
A fundamental open problem in monotone game theory is the computation of a specific generalized Nash equilibrium (GNE) among all the available ones, e. g. the optimal equilibrium with respect to a system-level objective.
Learning generalized Nash equilibria in monotone games: A hybrid adaptive extremum seeking control approach
In this paper, we solve the problem of learning a generalized Nash equilibrium (GNE) in merely monotone games.
An extremum seeking algorithm for monotone Nash equilibrium problems
In this paper we consider the problem of finding a Nash equilibrium (NE) via zeroth-order feedback information in games with merely monotone pseudogradient mapping.
Operationally-Safe Peer-to-Peer Energy Trading in Distribution Grids: A Game-Theoretic Market-Clearing Mechanism
In future distribution grids, prosumers (i. e., energy consumers with storage and/or production capabilities) will trade energy with each other and with the main grid.
Highway Traffic Control via Smart e-Mobility -- Part II: Dutch A13 Case Study
In this paper, we study how to alleviate highway traffic congestions by encouraging plug-in electric and hybrid vehicles to stop at charging stations around peak congestion times.
Highway Traffic Control via Smart e-Mobility -- Part I: Theory
In this paper, we study how to alleviate highway traffic congestion by encouraging plug-in hybrid and electric vehicles to stop at a charging station around peak congestion times.
Training Generative Adversarial Networks via stochastic Nash games
We also show convergence of an averaged variant of the SRFB algorithm to a neighborhood of the solution when only few samples are available.
A game-theoretic approach for Generative Adversarial Networks
Since the training can be cast as a stochastic Nash equilibrium problem, we rewrite it as a variational inequality and introduce an algorithm to compute an approximate solution.
