An Explanation Mechanism for Bayesian Inferencing Systems
This work addresses the need for better explanation facilities in expert systems, particularly for users questioning knowledge base meaning, though it appears incremental as it builds on existing Bayesian frameworks.
The paper tackled the problem of generating explanations for knowledge base content in Bayesian inferencing systems by proposing a new effect measure that satisfies specific properties, resulting in a detailed explanation facility for the Generalized Bayesian Inferencing System.
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.