Probabilistic Inference in Influence Diagrams
This work addresses a computational bottleneck in decision-making models for researchers in AI and operations research, but it is incremental as it builds on existing reduction methods.
The paper tackles the problem of evaluating influence diagrams by proposing a new reduction method to Bayesian network inference, which results in significantly easier inference problems compared to previous methods.
This paper is about reducing influence diagram (ID) evaluation into Bayesian network (BN) inference problems. Such reduction is interesting because it enables one to readily use one's favorite BN inference algorithm to efficiently evaluate IDs. Two such reduction methods have been proposed previously (Cooper 1988, Shachter and Peot 1992). This paper proposes a new method. The BN inference problems induced by the mew method are much easier to solve than those induced by the two previous methods.