Model-Based Diagnosis with Qualitative Temporal Uncertainty
This work addresses model-based diagnosis for dynamic systems, offering an incremental extension to handle temporal uncertainty in domains like medical diagnosis.
The paper tackles the problem of diagnosing dynamic systems by incorporating qualitative temporal uncertainty using Allen's interval relations, resulting in a framework that abstracts from the number of observations and time points to enable efficient computation.
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.