Distributed Equilibrium-Seeking in Target Coverage Games via Self-Configurable Networks under Limited Communication

2026-03-18

AI Summary

This work addresses the challenge of efficiently computing Nash equilibria in multi-agent cooperative monitoring of dynamically relocatable targets under communication constraints, where the large combinatorial action space renders equilibrium computation intractable. The authors model target coverage as a defender-attacker zero-sum game and propose, for the first time, a distributed self-configuring communication-aware sensing framework that integrates communication constraints with submodular game theory. By combining a coordination-value mechanism with distributed bandit-submodular optimization, the approach efficiently approximates an approximate Nash equilibrium under stringent bandwidth limitations. Theoretical analysis and simulations demonstrate that the proposed method significantly outperforms baseline strategies, achieving coverage and game-theoretic payoffs markedly closer to optimal performance.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Multiagent Systems: Mechanism Design
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We study a target coverage problem in which a team of sensing agents, operating under limited communication, must collaboratively monitor targets that may be adaptively repositioned by an attacker. We model this interaction as a zero-sum game between the sensing team (known as the defender) and the attacker. However, computing an exact Nash equilibrium (NE) for this game is computationally prohibitive as the action space of the defender grows exponentially with the number of sensors and their possible orientations. Exploiting the submodularity property of the game's utility function, we propose a distributed framework that enables agents to self-configure their communication neighborhoods under bandwidth constraints and collaboratively maximize the target coverage. We establish theoretical guarantees showing that the resulting sensing strategies converge to an approximate NE of the game. To our knowledge, this is the first distributed, communication-aware approach that scales effectively for games with combinatorial action spaces while explicitly incorporating communication constraints. To this end, we leverage the distributed bandit-submodular optimization framework and the notion of Value of Coordination that were introduced in [1]. Through simulations, we show that our approach attains near-optimal game value and higher target coverage compared to baselines.

Problem

• target coverage
• zero-sum game
• Nash equilibrium
• limited communication
• combinatorial action space

Innovation

• distributed equilibrium-seeking
• submodular optimization
• communication-constrained networks
• target coverage games
• approximate Nash equilibrium