Automating Knowledge Acquisition and Refinement for Decision Support: A Connectionist Inductive Inference Model*

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.

[1]  James V. Hansen,et al.  Inducing rules for expert system development: an example using default and bankruptcy data , 1988 .

[2]  Ting-Peng Liang,et al.  A composite approach to inducing knowledge for expert systems design , 1992 .

[3]  Barry G. Silverman,et al.  Survey of expert critiquing systems: practical and theoretical frontiers , 1992, CACM.

[4]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[5]  Terrence J. Sejnowski,et al.  Parallel Networks that Learn to Pronounce English Text , 1987, Complex Syst..

[6]  Terrence J. Sejnowski,et al.  Analysis of hidden units in a layered network trained to classify sonar targets , 1988, Neural Networks.

[7]  Vimla L. Patel,et al.  Knowledge Based Solution Strategies in Medical Reasoning , 1986, Cogn. Sci..

[8]  Sholom M. Weiss,et al.  Maximizing the Predictive Value of Production Rules , 1990, Artif. Intell..

[9]  Pi-Sheng Deng,et al.  Experimentation with a back-propagation neural network: An application to planning end user system development , 1993, Inf. Manag..

[10]  Kristian J. Hammond,et al.  CHEF: A Model of Case-Based Planning , 1986, AAAI.

[11]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[12]  R. Duda,et al.  Expert Systems Research. , 1983, Science.

[13]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[14]  William Bain,et al.  A Case-Based Reasoning System for Subjective Assessment , 1986, AAAI.

[15]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[16]  Robert R. Hoffman,et al.  The Problem of Extracting the Knowledge of Experts from the Perspective of Experimental Psychology , 1987, AI Mag..

[17]  Allen Newell,et al.  PAS-II: An Interactive Task-Free Version of an Automatic Protocol Analysis System , 1973, IEEE Transactions on Computers.

[18]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

[19]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[20]  Ritu Agarwal,et al.  Knowledge Acquisition Using Structured Interviewing: An Empirical Investigation , 1990, J. Manag. Inf. Syst..

[21]  Janet L. Kolodner,et al.  Extending Problem Solver Capabilities Through Case-Based Inference , 1987 .

[22]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[23]  Robert Hecht-Nielsen,et al.  Applications of counterpropagation networks , 1988, Neural Networks.

[24]  Barry G. Silverman.  Evaluating and Refining Expert Critiquing Systems: A Methodology* , 1992 .

[25]  M. Shaw,et al.  Using an Expert System with Inductive Learning to Evaluate Business Loans , 1988 .

[26]  Kenneth A. Kozar,et al.  Discourse Analysis for Knowledge Acquisition: The Coherence Method , 1990, J. Manag. Inf. Syst..

[27]  Kevin Knight,et al.  Connectionist ideas and algorithms , 1990, CACM.

[28]  Andrew S. Noetzel,et al.  Image processing and pattern recognition in ultrasonograms by backpropagation , 1990, Neural Networks.