NAIVE: A Method for Representing Uncertainty and Temporal Relationships in an Automated Reasoner
This work addresses uncertainty and temporal relationships in automated reasoning for medical systems, but it appears incremental as it builds on existing graph-based and probabilistic methods.
The paper tackles the problem of reasoning about nondeterministic dynamic systems, such as those in medicine, by introducing NAIVE, a knowledge representation language and inferencing process that uses probability density functions to propagate uncertainty, and it has been applied to develop medical knowledge bases with over 100 variables.
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.