Decision trees and influence diagrams are complementary views of a decision problem. Decision trees display the set of alternative values for each decision and chance variable as branches coming out of each node. The influence diagram shows the dependencies among the variables more clearly than the decision tree. The influence diagram is a much more compact representation.
A Bayesian network is an influence diagram having only uncertainty nodes.