We present a framework for sequential decision making in problems described
by graphical models. The setting is given by dependent discrete random
variables with associated costs or revenues. In our examples, the dependent
variables are the potential outcomes (oil, gas or dry) when drilling a
petroleum well. The goal is to develop an optimal selection strategy that
incorporates a chosen utility function within an approximated dynamic
programming scheme.