Model-Based Diagnosis with Qualitative Temporal Uncertainty
  In this paper we describe a framework for model-based diagnosis of dynamic
systems, which extends previous work in this field by using and expressing
temporal uncertainty in the form of qualitative interval relations a la Allen.
Based on a logical framework extended by qualitative and quantitative temporal
constraints we show how to describe behavioral models (both consistency- and
abductive-based), discuss how to use abstract observations and show how
abstract temporal diagnoses are computed. This yields an expressive framework,
which allows the representation of complex temporal behavior allowing us to
represent temporal uncertainty. Due to its abstraction capabilities computation
is made independent of the number of observations and time points in a temporal
setting. An example of hepatitis diagnosis is used throughout the paper.
