Case-Based Knowledge Representation
The representation of knowledge in terms of rules is fraught with theoretical problems, such as the justification of induction, the "right" way to do it, and the revision of knowledg ein face of contradictions. In this paper we argue that these problems, and especiallyt the inconsistency of "knowledge," are partly due to the fact that we pretend to know what in fact cannot be known. Rather than coping with thep roblems that explicit induction raises, we suggest to avoid it. Instead of formulating rules which we supposedly "know," we may make do with the knowledge of actual cases from our experience. Starting from this viewpoint, we continue tooderive Case-Based Decision Theory (CBDT), and propose it as a less ambitious, yet less problematic theory of knowledge representation. CBDT deals with decision making under uncertainty, and can be viewed as performing implicit induction, that is, as using past experience to make decisions, without resorting to the explicit formulation of rules. We discuss two levels on which implicit induction takes place, and the corresponding two roles that "rules" may have in case-based decision making. We also discuss the process of learning and the concept of "expertise" as they are reflected in our model.
|Date of creation:||May 1993|
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- Itzhak Gilboa & David Schmeidler, 1992.
"Case-Based Decision Theory,"
994, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- David Schmeidler, 1989.
"Subjective Probability and Expected Utility without Additivity,"
Levine's Working Paper Archive
7662, David K. Levine.
- Schmeidler, David, 1989. "Subjective Probability and Expected Utility without Additivity," Econometrica, Econometric Society, vol. 57(3), pages 571-587, May.
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