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