Report Number: CS-TR-87-1170
Institution: Stanford University, Department of Computer Science
Title: Viewing Knowledge Bases as Qualitative Models
Author: Clancey, William J.
Date: May 1986
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 AI 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.