Representing and Solving Asymmetric Bayesian Decision Problems
This work addresses the problem of efficiently modeling and solving asymmetric decision scenarios in Bayesian decision theory, which is incremental as it builds upon existing influence diagram frameworks.
The paper tackles the representation and solution of asymmetric Bayesian decision problems by introducing asymmetric influence diagrams, a formal framework that encodes asymmetry at the qualitative level for direct readability, and presents an algorithm that decomposes the problem into symmetric subproblems organized as a tree, solving them via propagation from leaves to root using existing methods.
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.