An Explanation Mechanism for Bayesian Inferencing Systems

TL;DR

This paper introduces an explanation mechanism for Bayesian inferencing systems that clarifies how events influence variables, enhancing understanding of the knowledge base in expert systems.

Contribution

It proposes a novel effect measure satisfying specific properties, forming the foundation for an explanation facility in Bayesian systems.

Findings

Effect measure helps generate meaningful explanations

Facility improves user understanding of Bayesian knowledge bases

Detailed description of the explanation mechanism provided

Abstract

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.