Diagnosis as Planning Revisited

Abstract

In discrete dynamical systems change results from actions.
As such, given a set of observations, diagnoses often take the
form of posited events that result in the observed behaviour.
In this paper we revisit formal characterizations of diagnosis,
and their relationship to planning. We do so from both
a theoretical and a computational perspective. In particular,
we extend the characterization of diagnosis to deal with the
case of incomplete information, and rich preferences. We
also explore the use of state-of-the-art planning technology
for the automated generation of diagnoses. Examining several
classes of diagnosis problems, we provide both proof of
concept and benchmark experiments, the latter showing superior
performance to a leading diagnosis engine. Our findings
help support the hypothesis that planning technology