The problem of assigning agents to tasks is a central computational challenge in many multi-agent autonomous systems. However, in the real world, agents are not always perfect and may fail due to a number of reasons. A motivating application is where the agents are robots that operate in the physical world and are susceptible to failures. This paper studies the problem of Robust Multi-Agent Task Assignment, which seeks to find an assignment that maximizes overall system performance while accounting for potential failures of the agents. We investigate both, stochastic and adversarial failures under this framework. For both cases, we present efficient algorithms that yield optimal or near-optimal results.

中文翻译：

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易失败和对抗性环境中的稳健多代理任务分配

为任务分配代理的问题是许多多代理自治系统中的核心计算挑战。然而，在现实世界中，代理并不总是完美的，并且可能由于多种原因而失败。一个激励应用程序是代理是在物理世界中运行的机器人，并且容易出现故障。本文研究了鲁棒多代理任务分配的问题，它试图找到一种分配，在考虑代理的潜在故障的同时，最大限度地提高整体系统性能。我们在这个框架下调查了随机和对抗性失败。对于这两种情况，我们提出了产生最佳或接近最佳结果的有效算法。