Model-Based Diagnosis with Qualitative Temporal Uncertainty

TL;DR

This paper introduces a framework for model-based diagnosis of dynamic systems that incorporates qualitative temporal uncertainty using Allen's interval relations, enabling expressive and efficient diagnosis of complex temporal behaviors.

Contribution

It extends existing diagnosis frameworks by integrating qualitative temporal uncertainty, allowing for more expressive modeling and diagnosis of dynamic systems with uncertain timing.

Findings

Framework effectively models complex temporal behaviors.

Diagnosis computation is independent of observation quantity.

Applied successfully to hepatitis diagnosis example.

Abstract

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.