  In this paper, we consider a robust action selection problem in multi-agent
systems where performance must be guaranteed when the system suffers a
worst-case attack on its agents. Specifically, agents are tasked with selecting
actions from a common ground set according to individualized objective
functions, and we aim to protect the system against attacks. In our problem
formulation, attackers attempt to disrupt the system by removing an agent's
contribution after knowing the system solution and thus can attack perfectly.
To protect the multi-agent system against such attacks, we aim to maximize the
minimum performance of all agents' individual objective functions under
attacks. Thus, we propose a fast algorithm with tunable parameters for
balancing complexity and performance, yielding substantially improved time
complexity and performance compared to recent methods. Finally, we provide
Monte Carlo simulations to demonstrate the performance of the proposed
algorithm.
