Consider a decision maker who is responsible to dynamically collect
observations
so as to enhance his information in a speedy manner about underlying phenomena
of interest while accounting for the penalty of wrong declaration. Due to the
sequential nature of the problem, the decision maker relies on his current
information state to adaptively select the highest “informative” sensing
action
among the available ones. Using results in dynamic programming, a lower bound
for the optimal total cost is established. Moreover, upper bounds are obtained
using heuristic policies for dynamic selection of actions. It is shown that
the
proposed heuristics achieve asymptotic optimality in many relevant problems
including the problems of variable-length coding with feedback and noisy
dynamic
search.