The Science and Engineering of Qualitative Models

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Citation: William J. Clancey. (1986) The Science and Engineering of Qualitative Models. In KSL-86-27, 1986.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author William J. Clancey
title The Science and Engineering of Qualitative Models
number KSL-86-27
institution Knowledge Systems, AI Laboratory
year 1986
Bibtex more
note STAN-CS-87-1170.
Access Paper
abstract The concept of a qualitative model provides a unifying perspective for understanding how expert systems differ from conventional programs. Knowledge bases contain qualitative models of systems in the world, that is, primarily non-numeric descriptions that provide a basis for explaining and predicting behavior and formulating action plans. The prevalent view that a qualitative model must be a simulation, to the exclusion of prototypic and behavioral descriptions, has fragmented our field, so that we have failed to usefully synthesize what we have learned about modeling processes. For example, our ideas about "scoring functions" and "causal network traversal," developed apart from a modeling perspective, have obscured the inherent explanatory nature of diagnosis. While knowledge engineering has greatly benefited from the study of human experts as a means of informing model construction, overemphasis on modeling the expert's knowledge has detracted from the primary objective of modeling a system in the world. Placing AI squarely in the evolutionary line of teleologic and topologic modeling, this paper argues that the study of network representations has established a foundation for a science and engineering of qualitative models.

KSL Technical Report ID: KSL-86-27
Facts about The Science and Engineering of Qualitative ModelsRDF feed
Abstract The concept of a qualitative model provide … The concept of a qualitative model provides a unifying perspective for understanding how expert systems differ from conventional programs. Knowledge bases contain qualitative models of systems in the world, that is, primarily non-numeric descriptions that provide a basis for explaining and predicting behavior and formulating action plans. The prevalent view that a qualitative model must be a simulation, to the exclusion of prototypic and behavioral descriptions, has fragmented our field, so that we have failed to usefully synthesize what we have learned about modeling processes. For example, our ideas about "scoring functions" and "causal network traversal," developed apart from a modeling perspective, have obscured the inherent explanatory nature of diagnosis. While knowledge engineering has greatly benefited from the study of human experts as a means of informing model construction, overemphasis on modeling the expert's knowledge has detracted from the primary objective of modeling a system in the world. Placing AI squarely in the evolutionary line of teleologic and topologic modeling, this paper argues that the study of network representations has established a foundation for a science and engineering of qualitative models. nce and engineering of qualitative models.
Author William J. Clancey  +
Bibtype techreport  +
Has author William J. Clancey  +
Has identifier KSL-86-27  +
Has publishing details 1986  +
Has title The Science and Engineering of Qualitative Models  +
Has where published KSL-86-27  +
Has year 1986  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-86-27  +
Note STAN-CS-87-1170.
Number KSL-86-27  +
Process note YES  +
Title The Science and Engineering of Qualitative Models  +
Year 1986  +
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