Knowledge Acquisition for Probabilistic Expert Systems

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Citation: Harold P. Lehmann. (1988) Knowledge Acquisition for Probabilistic Expert Systems. In KSL-88-40, 1988.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Harold P. Lehmann
title Knowledge Acquisition for Probabilistic Expert Systems
number KSL-88-40
institution Knowledge Systems, AI Laboratory
year 1988
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abstract Recent interest in probability-based expert systems has focused on the potential these systems have for being coherent with the beliefs of the modeled expert or of the user and consistent given any set of evidence. We have used the probabilistic formalism in creating the REFEREE system, a belief-network-based expert system designed to aid readers in determining the credibility of a randomized clinical trial. In this paper, we explore the effect the formalism had on the process of knowledge acquisition based on this experience. Although the system is still in development, we can report several of those effects. Specifically, the need to make operational definitions of concepts deemed important to the expert forced us to organize a domain that was formulated initially for a rule-based system. Categorizing probability distributions as being logical, probabilistic, or prototypical helped us to decrease the number of probability assessments. On the other hand, the lack of an intermediate prototype may have prolonged development, and computational limitations forced occasional compromises. The reality of building expert systems in a probabilistic paradigm may not be as hard as some critics have predicted.

KSL Technical Report ID: KSL-88-40
Facts about Knowledge Acquisition for Probabilistic Expert SystemsRDF feed
Abstract Recent interest in probability-based exper … Recent interest in probability-based expert systems has focused on the potential these systems have for being coherent with the beliefs of the modeled expert or of the user and consistent given any set of evidence. We have used the probabilistic formalism in creating the REFEREE system, a belief-network-based expert system designed to aid readers in determining the credibility of a randomized clinical trial. In this paper, we explore the effect the formalism had on the process of knowledge acquisition based on this experience. Although the system is still in development, we can report several of those effects. Specifically, the need to make operational definitions of concepts deemed important to the expert forced us to organize a domain that was formulated initially for a rule-based system. Categorizing probability distributions as being logical, probabilistic, or prototypical helped us to decrease the number of probability assessments. On the other hand, the lack of an intermediate prototype may have prolonged development, and computational limitations forced occasional compromises. The reality of building expert systems in a probabilistic paradigm may not be as hard as some critics have predicted. be as hard as some critics have predicted.
Author Harold P. Lehmann  +
Bibtype techreport  +
Has author Harold P. Lehmann  +
Has identifier KSL-88-40  +
Has publishing details 1988  +
Has title Knowledge Acquisition for Probabilistic Expert Systems  +
Has where published KSL-88-40  +
Has year 1988  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-88-40  +
Number KSL-88-40  +
Process note YES  +
Title Knowledge Acquisition for Probabilistic Expert Systems  +
Year 1988  +
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