From influence diagrams to multi-operator cluster DAGs
  There exist several architectures to solve influence diagrams using local
computations, such as the Shenoy-Shafer, the HUGIN, or the Lazy Propagation
architectures. They all extend usual variable elimination algorithms thanks to
the use of so-called 'potentials'. In this paper, we introduce a new
architecture, called the Multi-operator Cluster DAG architecture, which can
produce decompositions with an improved constrained induced-width, and
therefore induce potentially exponential gains. Its principle is to benefit
from the composite nature of influence diagrams, instead of using uniform
potentials, in order to better analyze the problem structure.
