Abstract
In constructing an expert system, there are usually several ways to represent a given piece of knowledge regardless of the knowledge representation formalism used. Initially, all of them may appear to be equivalent, but as the system evolves, it often becomes apparent that some are better than others, leading to the need to revise representations. Such revisions can be very time-consuming and prone to error. In this paper, we argue that the additional constraints imposed by the addition of an explanation facility can guide the creation of a knowledge base in a manner that reduces the need to subsequently re-structure the knowledge base as the system's functionality increases. We describe criteria that may be applied after the knowledge base is constructed to reveal potential weaknesses as well as those that may be employed during knowledge base construction. Finally, we briefly describe an expert system shell we have constructed that embodies these guiding principles, the Explainable Expert Systems framework.
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References
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© 1989 Springer-Verlag Berlin Heidelberg
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Swartout, W.R., Smoliar, S.W. (1989). Explanation: A source of guidance for knowledge representation. In: Morik, K. (eds) Knowledge Representation and Organization in Machine Learning. Lecture Notes in Computer Science, vol 347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017214
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DOI: https://doi.org/10.1007/BFb0017214
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