A Method for Using Belief Networks as Influence Diagrams
This work addresses a specific computational challenge in decision-making models for researchers in AI and probabilistic reasoning, but it appears incremental as it builds on existing belief-network methods.
The paper tackles the problem of solving influence diagram problems by demonstrating a method that applies both exact and approximate belief-network algorithms to them, with the result being a potential for more efficient influence diagram algorithm design through this relationship.
This paper demonstrates a method for using belief-network algorithms to solve influence diagram problems. In particular, both exact and approximation belief-network algorithms may be applied to solve influence-diagram problems. More generally, knowing the relationship between belief-network and influence-diagram problems may be useful in the design and development of more efficient influence diagram algorithms.