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Back-propagation learning in expert networks
R C Lacher1, S I Hruska, D C Kuncicky
1Dept. of Comput. Sci., Florida State Univ., Tallahassee, FL.
IEEE Transactions on Neural Networks
|January 1, 1992
Summary
This study introduces a novel back-propagation learning algorithm for expert networks, automating knowledge acquisition for certainty factors. This advancement enhances the efficiency of expert systems by simplifying the complex process of knowledge extraction.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Expert Systems
Background:
- Expert networks, derived from expert systems, utilize event-driven, acyclic neural objects.
- These objects employ complex nonlinear combining functions, distinct from standard neural network nodes.
Purpose of the Study:
- To develop a back-propagation learning algorithm for general acyclic, event-driven networks.
- To derive a specific learning algorithm for EMYCIN-derived expert networks.
- To automate knowledge acquisition for certainty factors in expert systems.
Main Methods:
- Developed a general back-propagation learning algorithm for acyclic, event-driven networks.
- Derived a specific algorithm integrating back-propagation with expert network features.
- Incorporated gradient calculation for nonlinear combining functions and the hypercube knowledge space.
Main Results:
- Successfully tested the learning algorithm on a 97-node expert network.
- Demonstrated automation of certainty factor acquisition, a challenging aspect of knowledge extraction.
- The algorithm effectively handles the complexities of expert network processing.
Conclusions:
- The developed algorithm offers automated knowledge acquisition for expert networks.
- This approach simplifies and enhances the extraction of certainty factors.
- The method is applicable to EMYCIN-derived expert networks and potentially others.
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