  Integrating diagnosis and repair is particularly crucial when gaining
sufficient information to discriminate between several candidate diagnoses
requires carrying out some repair actions. A typical case is supply restoration
in a faulty power distribution system. This problem, which is a major concern
for electricity distributors, features partial observability, and stochastic
repair actions which are more elaborate than simple replacement of components.
This paper analyses the difficulties in applying existing work on integrating
model-based diagnosis and repair and on planning in partially observable
stochastic domains to this real-world problem, and describes the pragmatic
approach we have retained so far.

Artificial Intelligence