6 research outputs found
Bayesian Quadratic Network Game Filters
A repeated network game where agents have quadratic utilities that depend on
information externalities -- an unknown underlying state -- as well as payoff
externalities -- the actions of all other agents in the network -- is
considered. Agents play Bayesian Nash Equilibrium strategies with respect to
their beliefs on the state of the world and the actions of all other nodes in
the network. These beliefs are refined over subsequent stages based on the
observed actions of neighboring peers. This paper introduces the Quadratic
Network Game (QNG) filter that agents can run locally to update their beliefs,
select corresponding optimal actions, and eventually learn a sufficient
statistic of the network's state. The QNG filter is demonstrated on a Cournot
market competition game and a coordination game to implement navigation of an
autonomous team
Two-Player Incomplete Games of Resilient Multiagent Systems
Evolution of agents' dynamics of multiagent systems under consensus protocol
in the face of jamming attacks is discussed, where centralized parties are able
to influence the control signals of the agents. In this paper we focus on a
game-theoretical approach of multiagent systems where the players have
incomplete information on their opponents' strength. We consider repeated games
with both simultaneous and sequential player actions where players update their
beliefs of each other over time. The effect of the players' optimal strategies
according to Bayesian Nash Equilibrium and Perfect Bayesian Equilibrium on
agents' consensus is examined. It is shown that an attacker with incomplete
knowledge may fail to prevent consensus despite having sufficient resources to
do so.Comment: 9 pages, 6 figures. Accepted in IFAC-WC 202
Maximizing Social Welfare and Agreement via Information Design in Linear-Quadratic-Gaussian Games
We consider linear-quadratic Gaussian (LQG) games in which players have
quadratic payoffs that depend on the players' actions and an unknown
payoff-relevant state, and signals on the state that follow a Gaussian
distribution conditional on the state realization. An information designer
decides the fidelity of information revealed to the players in order to
maximize the social welfare of the players or reduce the disagreement among
players' actions. Leveraging the semi-definiteness of the information design
problem, we derive analytical solutions for these objectives under specific LQG
games. We show that full information disclosure maximizes social welfare when
there is a common payoff-relevant state, when there is strategic
substitutability in the actions of players, or when the signals are public.
Numerical results show that as strategic substitution increases, the value of
the information disclosure increases. When the objective is to induce
conformity among players' actions, hiding information is optimal. Lastly, we
consider the information design objective that is a weighted combination of
social welfare and cohesiveness of players' actions. We obtain an interval for
the weights where full information disclosure is optimal under public signals
for games with strategic substitutability. Numerical solutions show that the
actual interval where full information disclosure is optimal gets close to the
analytical interval obtained as substitution increases
Scalable Learning In Distributed Robot Teams
Mobile robots are already in use for mapping, agriculture, entertainment, and the delivery of goods and people. As robotic systems continue to become more affordable, large numbers of mobile robots may be deployed concurrently to accomplish tasks faster and more efficiently. Practical deployments of very large teams will require scalable algorithms to enable the distributed cooperation of autonomous agents. This thesis focuses on the three main algorithmic obstacles to the scalability of robot teams: coordination, control, and communication. To address these challenges, we design graph-based abstractions that allow us to apply Graph Neural Networks (GNNs).First, a team of robots must continually coordinate to divide up mission requirements among all agents. We focus on the case studies of exploration and coverage to develop a spatial GNN controller that can coordinate a team of dozens of agents as they visit thousands of landmarks. A routing problem of this size is intractable for existing optimization-based approaches. Second, a robot in a team must be able to execute the trajectory that will accomplish its given sub-task. In large teams with high densities of robots, planning and execution of safe, collision-free trajectories requires the joint optimization over all agent trajectories, which may be impractical in large teams. We present two approaches to scalable control: a) a controller for flocking that uses delayed communication formalized via a GNN; and b) an inverse optimal planning method that learns from real air traffic data. Third, robot teams may need to operate in harsh environments without existing communication infrastructure, requiring the formation of ad-hoc networks to exchange information. Many algorithms for control of multi-robot teams operate under the assumption that low-latency, global state information necessary to coordinate agent actions can readily be disseminated among the team. Our approach leverages GNNs to control the connectivity within the ad-hoc network and to provide the data distribution infrastructure necessary for countless multi-robot algorithms. Finally, this thesis develops a framework for distributed learning to be used when centralized information is unavailable during training. Our approach allows robots to train controllers independently and then share their experiences by composing multiple models represented in a Reproducing Kernel Hilbert Space
IEEE Transactions On Signal Processing : Vol. 62, No. 9 - 12, May - June 2014
1. Secure beamforming for MIMO Two-Way communications with an untrusted relay.
2. Optimal power allocation for parameter tracking in a distributed amplify-and-forward sensor network.
3. Sparsity-aware shpere decoding: algorithms and complexity analysis.
4. Fourier-based transmit beampattern design using MIMO radar.
5. Stochastic analysis of the LMS and NLMS algorithms for cyclostationary white gaussian inputs.
6. Bayesian quadratic network game filters.
Etc