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.

翻译：本文研究一个目标覆盖问题：一组感知智能体在有限通信条件下，必须协同监控可能被攻击者自适应重新定位的目标。我们将该交互建模为感知团队（称为防御方）与攻击方之间的零和博弈。然而，由于防御方行动空间随传感器数量及其可能朝向呈指数级增长，计算该博弈的精确纳什均衡在计算上是不可行的。利用博弈效用函数的次模性质，我们提出一个分布式框架，使智能体能够在带宽约束下自配置其通信邻域，并协同最大化目标覆盖率。我们建立了理论保证，证明所生成的感知策略收敛于博弈的近似纳什均衡。据我们所知，这是首个分布式、通信感知的方法，能有效扩展至具有组合行动空间的博弈，同时显式纳入通信约束。为此，我们借鉴了文献[1]提出的分布式bandit次模优化框架及协调价值概念。通过仿真实验，我们证明相较于基线方法，本方法能获得接近最优的博弈值及更高的目标覆盖率。