Solving Hybrid Influence Diagrams with Deterministic Variables
This work addresses a specific technical challenge in probabilistic graphical models for researchers in AI and decision theory, but it appears incremental as it extends existing methods.
The authors tackled the problem of solving hybrid influence diagrams that include deterministic chance variables, and they developed an extended algorithm based on Shenoy's fusion method, demonstrating it on two small examples.
We describe a framework and an algorithm for solving hybrid influence diagrams with discrete, continuous, and deterministic chance variables, and discrete and continuous decision variables. A continuous chance variable in an influence diagram is said to be deterministic if its conditional distributions have zero variances. The solution algorithm is an extension of Shenoy's fusion algorithm for discrete influence diagrams. We describe an extended Shenoy-Shafer architecture for propagation of discrete, continuous, and utility potentials in hybrid influence diagrams that include deterministic chance variables. The algorithm and framework are illustrated by solving two small examples.