Premium
Automating Knowledge Acquisition and Refinement for Decision Support: A Connectionist Inductive Inference Model *
Author(s) -
Deng PiSheng
Publication year - 1993
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1993.tb00479.x
Subject(s) - computer science , connectionism , knowledge acquisition , inference , heuristic , artificial intelligence , expert system , decision support system , machine learning , knowledge based systems , artificial neural network , knowledge management , management science , economics
An important application of expert systems technology is to provide support for nonstructured decision making. Usually, nonstructured decision making is characterized by heavy reliance on heuristic knowledge, which is very difficult to articulate or document, and therefore traditional knowledge acquisition approaches are not very successful. The quality and effectiveness of an expert system supporting unstructured decision making is affected when traditional knowledge acquisition approaches are used. To alleviate this problem a model is proposed that combines inductive inference and neural network computing, and an example is presented that illustrates the potential of this model in unstructured decision support.