Knowledge-Based Decision Model Construction for Hierarchical Diagnosis:
  A Preliminary Report
  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.
