  A novel sequential change detection problem is proposed, in which the goal is
to not only detect but also accelerate the change. Specifically, it is assumed
that the sequentially collected observations are responses to treatments
selected in real time. The assigned treatments determine the pre-change and
post-change distributions of the responses and also influence when the change
happens. The goal is to find a treatment assignment rule and a stopping rule
that minimize the expected total number of observations subject to a
user-specified bound on the false alarm probability. The optimal solution is
obtained under a general Markovian change-point model. Moreover, an alternative
procedure is proposed, whose applicability is not restricted to Markovian
change-point models and whose design requires minimal computation. For a large
class of change-point models, the proposed procedure is shown to achieve the
optimal performance in an asymptotic sense. Finally, its performance is found
in simulation studies to be comparable to the optimal, uniformly with respect
to the error probability.
