  We study the achievable performance of adaptive query procedures for the
noisy 20 questions problem with measurement-dependent noise over a unit cube of
finite dimension. The performance criterion that we consider is the minimal
resolution, defined as the $L_\infty$ norm between the estimated and the true
values of the random location vector of a target, given a finite number of
queries constrained by an excess-resolution probability. Specifically, we
derive the achievable resolution of an adaptive query procedure based on the
variable length feedback code by Polyanskiy \emph{et al.} (TIT 2011).
Furthermore, we verify our theoretical results with numerical simulations and
compare the performance of our considered adaptive query procedure with that of
certain state-of-the-art algorithms, such as the sorted posterior matching
algorithm by Chiu and Javadi (ITW 2016). In particular, we demonstrate that the
termination strategy adopted in our adaptive query procedure can significantly
enhance the asymptotic performance of adaptive query procedures, especially at
moderate to large excess-resolution probability constraints.
