  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.
