The tutorial provides a survey on probabilistic decision graphs for modeling and solving decision problems under uncertainty. I shall give an introduction to influence diagrams, which is a popular framework for representing and solving sequential decision problems with a single decision maker. As the methods for solving influence diagrams can scale rather badly in the length of the decision sequence, I shall present a couple of approaches for calculating approximate solutions. The modeling scope of the influence diagram is limited to so-called symmetric decision problems. This limitation has motivated the development of alternative representation languages, which enlarge the class of decision problems that can be modeled efficiently. I shall present some of these alternative frameworks and demonstrate their expressibility using several examples.