Solving Asymmetric Decision Problems with Influence Diagrams
This work solves a computational bottleneck for researchers and practitioners using influence diagrams in decision analysis, but it is incremental as it builds on existing methods.
The paper addresses the inefficiency of evaluating symmetrized influence diagrams in asymmetric Bayesian decision problems by presenting an approach to avoid unnecessary computation.
While influence diagrams have many advantages as a representation framework for Bayesian decision problems, they have a serious drawback in handling asymmetric decision problems. To be represented in an influence diagram, an asymmetric decision problem must be symmetrized. A considerable amount of unnecessary computation may be involved when a symmetrized influence diagram is evaluated by conventional algorithms. In this paper we present an approach for avoiding such unnecessary computation in influence diagram evaluation.