Abstract
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.
This research was supported by an ERCIM Post-Doctoral Fellowship 94–05. Thanks to Cindy Wolverton for the signal processing example of Sec. 3, and to Myles Chippendale and Gerhard Wickler for helpful comments on an earlier draft of this paper.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
P. Gautier and T. Gruber. Generating explanations of device behavior using compositional modeling and causal ordering. In AAAI-93, pages 264–270, 1993.
I. Goldstein. The genetic graph: A representation for the evolution of procedural knowledge. Int. J. Man-Machine Studies, 11:51–77, 1979.
J. Wallis and E. Shortliffe. Customized explanations using causal knowledge. In Buchanan and Shortliffe, editors, Rule-Based Expert Systems. Addison-Wesley, 1984.
M. Wick and W. Thompson. Reconstructive expert system explanation. Artificial Intelligence, 54:33–70, 1992.
I. Zukerman and R. McConachy. Generating discourse across several user models: Maximizing belief while avoiding boredom and overload. In IJCAI-95, 1995.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wolverton, M. (1995). Presenting significant information in expert system explanation. In: Pinto-Ferreira, C., Mamede, N.J. (eds) Progress in Artificial Intelligence. EPIA 1995. Lecture Notes in Computer Science, vol 990. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60428-6_39
Download citation
DOI: https://doi.org/10.1007/3-540-60428-6_39
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60428-0
Online ISBN: 978-3-540-45595-0
eBook Packages: Springer Book Archive