Presenting significant information in expert system explanation
This paper presents a method for eliminating insignificant portions of an explanation of a conclusion — those portions that include terminology and inferences that the user does not have the expertise to understand, and those portions that add little to the user's belief in the conclusion. The method exploits a user model to select for presentation only those portions of an expert system's reasoning that add significantly to the user's belief that the conclusion is the right one. Examples demonstrate how the method generates concise explanations with only significant information, and how it tailors the explanation to the user.
Unable to display preview. Download preview PDF.
- 1.P. Gautier and T. Gruber. Generating explanations of device behavior using compositional modeling and causal ordering. In AAAI-93, pages 264–270, 1993.Google Scholar
- 2.I. Goldstein. The genetic graph: A representation for the evolution of procedural knowledge. Int. J. Man-Machine Studies, 11:51–77, 1979.Google Scholar
- 3.J. Wallis and E. Shortliffe. Customized explanations using causal knowledge. In Buchanan and Shortliffe, editors, Rule-Based Expert Systems. Addison-Wesley, 1984.Google Scholar
- 5.I. Zukerman and R. McConachy. Generating discourse across several user models: Maximizing belief while avoiding boredom and overload. In IJCAI-95, 1995.Google Scholar