延迟和切换代价下的多局异常搜索
Anomaly Search with Multiple Plays under Delay and Switching Costs
摘要：
研究了在$M$过程中寻找$L$异常过程的问题。在每一次，决策者可以观察$K$过程的子集（即，多个播放）。在观察过程时，根据过程是正常还是异常，测量值遵循两种不同分布之一。我们的目标是设计一个政策，最大限度地减少贝叶斯风险之间的平衡样本的复杂性，检测错误，切换成本与切换过程。我们制定了一项政策，称为连续控制传感（CCS），以实现这一目标。一方面，通过对比现有的研究控制感测，CCS的政策感测过程连续，以减少切换成本。另一方面，该策略以闭环方式控制感测操作，以在必要时在进程之间切换，以保证可靠的推断。我们从理论上证明，CCS是渐近最优的最小化贝叶斯风险的检测误差接近零（即，样本复杂度增加）。仿真结果表明，CCS的强大性能，以及在有限的制度。
英文摘要：
The problem of searching for $L$ anomalous processes among $M$ processes is considered. At each time, the decision maker can observe a subset of $K$ processes (i.e., multiple plays). The measurement drawn when observing a process follows one of two different distributions, depending on whether the process is normal or abnormal. The goal is to design a policy that minimizes the Bayes risk which balances between the sample complexity, detection errors, and the switching cost associated with switching across processes. We develop a policy, dubbed consecutive controlled sensing (CCS), to achieve this goal. On the one hand, by contrast to existing studies on controlled sensing, the CCS policy senses processes consecutively to reduce the switching cost. On the other hand, the policy controls the sensing operation in a closed-loop manner to switch between processes when necessary to guarantee reliable inference. We prove theoretically that CCS is asymptotically optimal in terms of minimizing the Bayes risk as the detection error approaches zero (i.e., the sample complexity increases). Simulation results demonstrate strong performance of CCS in the finite regime as well.
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