Title

Iasknot: A Simulation-Based Object-Oriented Framework For The Acquisition Of Implicit Expert Knowledge

Abstract

Research in the field of Artificial Intelligence (AI) aims to embed aspects of human intelligence in the computer. Several factors constrain the development of a true intelligent autonomous machine. The acquisition of expert knowledge continues to hinder progress. Knowledge acquisition techniques have reduced the effort involved in acquiring knowledge from an expert and representing it in a form that can be used by the computer. Most, however, focus on the gathering and representation of one class of knowledge. Two major categories of expertise makeup most of the expert's knowledge: explicit knowledge which is easy to articulate, and implicit knowledge such as intuition and judgment. It is in the nature of implicit knowledge that makes it difficult to clearly define and acquire from experts. Most current approaches learn only the expert explicit knowledge via query sessions and ignore the implicit expertise altogether. Humans, on the other hand, continually learn and apply both types of knowledge. We typically learn the implicit knowledge by observing others handle real-life situations and by adapting what we observed to handle new situations.

Publication Date

12-1-1995

Publication Title

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

Volume

3

Number of Pages

2428-2433

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

0029509751 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0029509751

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