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KNOWLEDGE ACQUISITION AS A CONSTRUCTIVE MODELING ACTIVITY

delete1993-01-01
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PRE
AI
K
Kenneth M. Ford *
J
Jeffrey M. Bradshaw
A
ADAMSWEBBER, JR
DOI:10.1002/int.4550080103delete
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摘要

摘要

En 中文
Knowledge acquisition is a constructive modeling process, not simply a matter of ''expertise transfer.'' Consistent with this perspective, we advocate knowledge acquisition practices and tools that facilitate active collaboration between expert and knowledge engineer, that exploit a serviceable theory in their application. and that support knowledge-based system development from a life-cycle perspective. A constructivist theory of knowledge is offered as a plausible theoretical foundation for knowledge acquisition and as an effective practical approach to the dynamics of modeling. In this view, human experts construct knowledge from their own personal experiences while interacting with their social constituencies (e.g., supervisors, colleagues, clients, patients) in their niche of expertise. Knowledge acquisition is presented as a cooperative enterprise in which the knowledge engineer and expert collaborate in constructing an explicit model of problem solving in a specific domain. From this perspective, the agenda for the knowledge acquisition research community includes developing tools and methods to aid experts in their efforts to express, elaborate, and improve their models of the domain. This functional view of expertise helps account for several problems that typically arise in practical knowledge acquisition projects, many of which stem directly from the inadequacies of representations used at various stages of system development. To counter these problems, we emphasize the use of mediating representations as a means of communication between expert and knowledge engineer, and intermediate representations to help bridge the gap between the mediating representations themselves, as well as between the mediating representations and a particular implementation formalism.
Keyword:
EXPERT SYSTEMS
FOUNDATIONS
PSYCHOLOGY
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期刊

International Journal of Intelligent Systems 封面图
International Journal of Intelligent Systems
IF:
3.7
论文数:
3.1K
被引数:
8.1K

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