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.

翻译：在本文中,我们考虑多试剂系统中的强力行动选择问题,在多试剂系统中,当系统受到最坏的打击时,必须保证其性能。具体地说,代理人的任务是从一个根据个性化客观功能设定的共同点中选择行动,我们的目标是保护系统免遭攻击。在我们的问题提法中,攻击者试图通过在了解系统解决方案后取消代理人的贡献来破坏系统,从而可以完美地攻击。为了保护多试剂系统,我们的目标是最大限度地提高所有代理人在攻击中个别客观功能的最低限度性能。因此,我们提出一种快速算法,配有可以平衡复杂性和性能的可追踪参数,比最近的方法大大改进时间复杂性和性能。最后,我们提供蒙特卡洛模拟,以展示拟议算法的性能。