Accession Number : ADA187091

Title :   Viewing Knowledge Bases as Qualitative Models.

Descriptive Note : Technical rept.,

Corporate Author : STANFORD UNIV CA KNOWLEDGE SYSTEMS LAB

Personal Author(s) : Clancey, William J

PDF Url : ADA187091

Report Date : May 1986

Pagination or Media Count : 32

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 casual 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 artificial intelligence squarely in the evolutionary line of telelogic and topologic modeling, this talk argues that the study of network representations has established a foundation for a science and engineering of qualitative models.

Descriptors :   *COMPUTER PROGRAMS, *DATA BASES, *MEDICAL COMPUTER APPLICATIONS, *SYSTEMS ENGINEERING, ARTIFICIAL INTELLIGENCE, BEHAVIOR, CONSTRUCTION, NETWORKS, SCORING, COMPUTERIZED SIMULATION, DIAGNOSIS(MEDICINE)

Subject Categories : Computer Programming and Software
      Medicine and Medical Research

Distribution Statement : APPROVED FOR PUBLIC RELEASE