A Method for Using Belief Networks as Influence Diagrams

TL;DR

This paper presents a method to leverage belief-network algorithms for solving influence diagram problems, potentially leading to more efficient algorithms by understanding their relationship.

Contribution

It introduces a novel approach to use belief networks for influence diagram solutions, bridging the two frameworks for improved algorithm design.

Findings

Both exact and approximate belief-network algorithms can solve influence diagram problems.

Understanding the relationship between belief networks and influence diagrams can enhance algorithm efficiency.

The method facilitates the application of belief-network algorithms to influence diagram problems.

Abstract

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.