From influence diagrams to multi-operator cluster DAGs
This work addresses computational bottlenecks in probabilistic graphical models for researchers and practitioners, representing an incremental improvement over existing architectures.
The paper tackles the problem of solving influence diagrams by introducing a new Multi-operator Cluster DAG architecture that improves constrained induced-width, leading to potentially exponential gains in computational efficiency.
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.