  This paper deals with the question of how to most effectively conduct
experiments in Partially Observed Markov Decision Processes so as to provide
data that is most informative about a parameter of interest. Methods from
Markov decision processes, especially dynamic programming, are introduced and
then used in an algorithm to maximize a relevant Fisher Information. The
algorithm is then applied to two POMDP examples. The methods developed can also
be applied to stochastic dynamical systems, by suitable discretization, and we
consequently show what control policies look like in the Morris-Lecar Neuron
model, and simulation results are presented. We discuss how parameter
dependence within these methods can be dealt with by the use of priors, and
develop tools to update control policies online. This is demonstrated in
another stochastic dynamical system describing growth dynamics of DNA template
in a PCR model.
