  This paper deals with the representation and solution of asymmetric Bayesian
decision problems. We present a formal framework, termed asymmetric influence
diagrams, that is based on the influence diagram and allows an efficient
representation of asymmetric decision problems. As opposed to existing
frameworks, the asymmetric influece diagram primarily encodes asymmetry at the
qualitative level and it can therefore be read directly from the model. We give
an algorithm for solving asymmetric influence diagrams. The algorithm initially
decomposes the asymmetric decision problem into a structure of symmetric
subproblems organized as a tree. A solution to the decision problem can then be
found by propagating from the leaves toward the root using existing evaluation
methods to solve the sub-problems.
