Dynamic Network Updating Techniques For Diagnostic Reasoning

TL;DR

The paper introduces DYNASTY, a probabilistic influence diagram system for dynamic diagnostic reasoning that updates networks over time based on observations and sensitivity analysis.

Contribution

It presents a novel influence diagram-based approach with algorithms for dynamically updating diagnostic networks in response to changing data.

Findings

Effective sensitivity analysis for network validation

Algorithms successfully update network topology dynamically

Decision thresholds improve diagnostic accuracy

Abstract

A new probabilistic network construction system, DYNASTY, is proposed for diagnostic reasoning given variables whose probabilities change over time. Diagnostic reasoning is formulated as a sequential stochastic process, and is modeled using influence diagrams. Given a set O of observations, DYNASTY creates an influence diagram in order to devise the best action given O. Sensitivity analyses are conducted to determine if the best network has been created, given the uncertainty in network parameters and topology. DYNASTY uses an equivalence class approach to provide decision thresholds for the sensitivity analysis. This equivalence-class approach to diagnostic reasoning differentiates diagnoses only if the required actions are different. A set of network-topology updating algorithms are proposed for dynamically updating the network when necessary.