  Sequential decision making under uncertainty is studied in a mixed
observability domain. The goal is to maximize the amount of information
obtained on a partially observable stochastic process under constraints imposed
by a fully observable internal state. An upper bound for the optimal value
function is derived by relaxing constraints. We identify conditions under which
the relaxed problem is a multi-armed bandit whose optimal policy is easily
computable. The upper bound is applied to prune the search space in the
original problem, and the effect on solution quality is assessed via simulation
experiments. Empirical results show effective pruning of the search space in a
target monitoring domain.
