NAIVE: A Method for Representing Uncertainty and Temporal Relationships in an Automated Reasoner

TL;DR

NAIVE is a knowledge representation language and inference system designed for reasoning about uncertain, dynamic systems such as those in medicine, using probabilistic graphs to model variables and their relationships over time.

Contribution

The paper introduces NAIVE, a novel low-level language and inference process for representing and reasoning about uncertainty and temporal relationships in dynamic systems.

Findings

Successfully modeled over 100 medical variables.

Effectively propagated uncertainty using probability density functions.

Applied to medical knowledge bases for dynamic reasoning.

Abstract

This paper describes NAIVE, a low-level knowledge representation language and inferencing process. NAIVE has been designed for reasoning about nondeterministic dynamic systems like those found in medicine. Knowledge is represented in a graph structure consisting of nodes, which correspond to the variables describing the system of interest, and arcs, which correspond to the procedures used to infer the value of a variable from the values of other variables. The value of a variable can be determined at an instant in time, over a time interval or for a series of times. Information about the value of a variable is expressed as a probability density function which quantifies the likelihood of each possible value. The inferencing process uses these probability density functions to propagate uncertainty. NAIVE has been used to develop medical knowledge bases including over 100 variables.