Distributed Equilibrium-Seeking in Target Coverage Games via Self-Configurable Networks under Limited Communication
This addresses the challenge of scalable and communication-aware coordination for sensing agents in adversarial environments, representing an incremental advance by building on existing distributed optimization frameworks.
The paper tackles the problem of distributed equilibrium-seeking in target coverage games with limited communication, proposing a self-configurable network framework that converges to an approximate Nash equilibrium and achieves near-optimal game value and higher target coverage in simulations.
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.