Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? One of the most critical post-disruption processes after a natural disaster is to quickly determine the damage or failure states of critical infrastructure components. However, this is non-trivial, considering that often only a fraction of components may be accessible or observable after a disruptive event. Past work has looked into inferring failed components given point probes, i.e. with a direct sample of failed components. In contrast, we study the harder problem of inferring failed components given partial information of some `serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain. We formulate this novel problem using the Minimum Description Length (MDL) principle, and then present a greedy algorithm that minimizes MDL cost effectively. We evaluate our algorithm on domain-expert simulations of real networks in the aftermath of an earthquake. Our algorithm successfully identify failed components, especially the critical ones affecting the overall system performance.
翻译:我们能否从供应节点的可达节点样本中推断出基础设施网络中所有失败的部件?自然灾害发生后最关键的中断后过程之一是迅速确定关键基础设施部件的损坏或故障状态。然而,这是非三重性的,考虑到在破坏性事件之后,通常只有一小部分部件可以进入或观测到。过去的工作已经研究过如何推断出给定点探测器的失败部件,即直接抽样的失败部件。相反,我们研究的是,在一些“可使用”可达节点和一小块点探测器的局部信息中,推断失败部件的较困难的问题,这是第一个往往更实际获得的。我们使用最低描述长度原则来拟订这个新问题,然后提出一种贪婪的算法,有效地将MDL成本降到最低。我们评估了地震后实际网络的域专家模拟的算法。我们的算法成功地查明了失败的部件,特别是影响整个系统运行的关键部件。