Many industrial and security applications employ a suite of sensors for detecting abrupt changes in temporal behavior patterns. These abrupt changes typically manifest locally, rendering only a small subset of sensors informative. Continuous monitoring of every sensor can be expensive due to resource constraints, and serves as a motivation for the bandit quickest changepoint detection problem, where sensing actions (or sensors) are sequentially chosen, and only measurements corresponding to chosen actions are observed. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We then propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions. We derive expected delay bounds for the proposed scheme and show that these bounds match our information-theoretic lower bounds at low false alarm rates, establishing optimality of the proposed method. We then perform a number of experiments on synthetic and real datasets demonstrating the effectiveness of our proposed method.

翻译：许多工业和安全应用使用一组传感器来检测时间行为模式中的突然变化。这些突然变化通常局部发生，仅使少量传感器具有信息量。由于资源限制，持续监控每个传感器可能成本高昂，这激发了强盗快速变化点检测问题，其中感测动作（或传感器）被顺序选择，并且仅观察到与所选动作对应的测量值。我们推导了针对一般有限参数化概率分布类的检测延迟的信息论下界。然后，我们提出了一种计算高效的在线感测方案，该方案无缝平衡了探索不同感测选项的需求与查询信息性动作的利用。我们推导了所提方案的预期延迟界，并表明在低虚警率下，这些界与我们的信息论下界匹配，从而建立了所提方法的最优性。随后，我们在合成和真实数据集上进行了多项实验，证明了所提方法的有效性。