  Numerous methods for probabilistic reasoning in large, complex belief or
decision networks are currently being developed. There has been little research
on automating the dynamic, incremental construction of decision models. A
uniform value-driven method of decision model construction is proposed for the
hierarchical complete diagnosis. Hierarchical complete diagnostic reasoning is
formulated as a stochastic process and modeled using influence diagrams. Given
observations, this method creates decision models in order to obtain the best
actions sequentially for locating and repairing a fault at minimum cost. This
method construct decision models incrementally, interleaving probe actions with
model construction and evaluation. The method treats meta-level and baselevel
tasks uniformly. That is, the method takes a decision-theoretic look at the
control of search in causal pathways and structural hierarchies.
