An Explanation Mechanism for Bayesian Inferencing Systems
  Explanation facilities are a particularly important feature of expert system
frameworks. It is an area in which traditional rule-based expert system
frameworks have had mixed results. While explanations about control are well
handled, facilities are needed for generating better explanations concerning
knowledge base content. This paper approaches the explanation problem by
examining the effect an event has on a variable of interest within a symmetric
Bayesian inferencing system. We argue that any effect measure operating in this
context must satisfy certain properties. Such a measure is proposed. It forms
the basis for an explanation facility which allows the user of the Generalized
Bayesian Inferencing System to question the meaning of the knowledge base. That
facility is described in detail.
