Variable elimination for influence diagrams with super value nodes

In the original formulation of influence diagrams (IDs), each model contained exactly one utility node. Tatman and Shachter (1990), introduced the possibility of having super-value nodes that represent a combination of their parents' utility functions. They also proposed an arc reversal algorithm for IDs with super-value nodes, which has two shortcomings: it requires dividing potentials when reversing arcs, and it tends to introduce redundant (i.e., unnecessary) variables in the resulting policies. In this paper we propose a variable-elimination algorithm for influence diagrams with super-value nodes that in general introduces fewer redundant variables, is faster, requires less memory, may simplify sensitivity analysis, and can speed-up inference in IDs containing canonical models, such as the noisy OR. Contents

• Type: Journal
• Year: 2010
• Where: IJAR
• Authors: Manuel Luque, Francisco Javier Díez
• Keywords: Arc Reversal Algorithm, Fewer Redundant Variables, IJAR 2010, Influence Diagrams