An Algorithm for Resilient Nash Equilibrium Seeking in the Partial Information Setting

Current research in distributed Nash equilibrium (NE) seeking in the partial information setting assumes that information is exchanged between agents that are "truthful". However, in general noncooperative games agents may consider sending misinformation to neighboring agents with the goal...

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Bibliographic Details
Published in:IEEE transactions on control of network systems Vol. 10; no. 4; pp. 1 - 10
Main Authors: Gadjov, Dian, Pavel, Lacra
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Communication
Communication networks
Control systems
Costs
Distributed algorithms/Control
Drones
Game theory
Games
Indexes
Nash equilibrium
Network systems
nonlinear systems
optimization
Random signals
Reagents
sensor networks
ISSN:2325-5870, 2372-2533
Online Access:Get full text
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Summary:Current research in distributed Nash equilibrium (NE) seeking in the partial information setting assumes that information is exchanged between agents that are "truthful". However, in general noncooperative games agents may consider sending misinformation to neighboring agents with the goal of further reducing their cost. Additionally, communication networks are vulnerable to attacks from agents outside the game as well as communication failures. In this paper, we propose a distributed NE seeking algorithm that is robust against adversarial agents that transmit noise, random signals, constant singles, deceitful messages, as well as being resilient to external factors such as dropped communication, jammed signals, and man in the middle attacks. The core issue that makes the problem challenging is that agents have no means of verifying if the information they receive is correct, i.e. there is no "ground truth". To address this problem, we use an observation graph, that gives truthful action information, in conjunction with a communication graph, that gives (potentially incorrect) information. By filtering information obtained from these two graphs, we show that our algorithm is resilient against adversarial agents and converges to the Nash equilibrium.
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ISSN:2325-5870
2372-2533
DOI:10.1109/TCNS.2023.3264749