论文标题
强大的多代理任务分配在容易失败和对抗环境中
Robust Multi-Agent Task Assignment in Failure-Prone and Adversarial Environments
论文作者
论文摘要
将代理分配给任务的问题是许多多代理自治系统中的中心计算挑战。但是，在现实世界中，代理商并不总是完美的，并且由于多种原因可能会失败。一个激励的应用是代理商是在物理世界中运行并容易失败的机器人。本文研究了强大的多代理任务分配的问题，该问题旨在找到一项最大化整体性能的任务，同时考虑到代理商的潜在故障。我们在此框架下研究了，随机和对抗性失败。在这两种情况下，我们都会提出有效的算法，这些算法产生最佳或近乎最佳的结果。
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.
