An Inference Technique for Integrating Knowledge from Disparate Sources

Abstract

This paper introduces a formal method for integrating knowledge derived from a variely of sources for use in "perceptual reasoning." The formalism is based on the "evidential propositional calculus," a derivative of Shafer's mathematical theory of evidence [4]. It is more general than either a Boolean or Bayesian approach, providing for Boolean and Bayesian inferencing when the appropriate information is available. In this formalism, the likelihood of a proposition A is represented as a subinterval, [s(A),p(A)], of the unit interval, [0,1], The evidential support for proposition A is represented by s(A), while p(A) represents its degree of plausibility; p(A) can also be interpreted as the degree to which one fails to doubt A, p(A) being equal to one minus the evidential support for -A. This paper describes how evidential information, furnished by a knowledge source in the form of a probability "mass" distribution, can be converted to this interval representation; how, through a set of inference rules for computing intervals of dependent propositions, this information can be extrapolated from those propositions it directly bears upon, to those it indirectly bears upon; and how multiple bodies of evidential information can be pooled. A sample application of this approach, modeling the operation of a collection of sensors (a particular type of knowledge source), illustrates these techniques.