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

A team from Purdue University led by Jayanth Bhargav, Zirui Xu, and Vasileios Tzoumas has developed a novel distributed framework for solving complex target coverage games where teams of sensing agents must collaboratively monitor targets that can be repositioned by an attacker. The research addresses a fundamental challenge in multi-agent systems: how to coordinate sensor networks under severe communication constraints while facing adversarial opponents. The problem is modeled as a zero-sum game where the defender (sensing team) and attacker compete, with the defender's action space growing exponentially with sensor count and orientation possibilities—making traditional Nash equilibrium computation computationally prohibitive.

The breakthrough comes from exploiting the submodularity property of the game's utility function, which allows the team to develop a distributed bandit-submodular optimization approach. Agents can self-configure their communication neighborhoods under strict bandwidth limitations while collaboratively maximizing target coverage. The framework incorporates the novel concept of "Value of Coordination" to balance local decisions with global objectives. Theoretical guarantees prove the system converges to approximate Nash equilibria, and simulations demonstrate it achieves near-optimal game value while outperforming baseline methods in coverage effectiveness.

This represents the first distributed, communication-aware approach that scales effectively for games with combinatorial action spaces while explicitly incorporating communication constraints. The methodology has significant implications for real-world applications including drone surveillance networks, autonomous vehicle coordination, and distributed sensor systems where bandwidth is limited and adversaries are adaptive. The research bridges game theory, optimization, and multi-agent systems to create practical solutions for complex coordination problems.