Thinking Backward for Knowledge Acquisition
AbstractThis article examines the direction in which knowledge bases are constructed for diagnosis and decision making. When building an expert system, it is traditional to elicit knowledge from an expert in the direction in which the knowledge is to be applied, namely, from observable evidence toward unobservable hypotheses. However, experts usually find it simpler to reason in the opposite direction-from hypotheses to unobservable evidence-because this direction reflects causal relationships. Therefore, we argue that a knowledge base be constructed following the expert's natural reasoning direction, and then reverse the direction for use. This choice of representation direction facilitates knowledge acquisition in deterministic domains and is essential when a problem involves uncertainty. We illustrate this concept with influence diagrams, a methodology for graphically representing a joint probability distribution. Influence diagrams provide a practical means by which an expert can characterize the qualitative and quantitative relationships among evidence and hypotheses in the apporiate direction. Once constructed, the relationships can easily be reserved into the less intuitive direction in order to perform inference inference and diagnosis. In this way, knowledge acquisition is made cognitively simple; the machine carries the burden of translating the representation.
How to Cite
Schachter, R. D., & Heckerman, D. (1987). Thinking Backward for Knowledge Acquisition. AI Magazine, 8(3), 55. https://doi.org/10.1609/aimag.v8i3.600
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