Title :
Resuscitation of certainty factors in expert networks
Author :
Hruska, S.I. ; Kuncicky, D.C. ; Lacher, R.C.
Author_Institution :
Dept. of Comput. Sci., Florida State Univ., Tallahassee, FL, USA
Abstract :
An expert network is a form of neural network which captures the rule-based knowledge of an expert system in digraph form. Connectionist learning techniques have been developed for these hybrid systems. In the present work, a study of the performance of these training algorithms in recovering certainty factors for expert networks is presented. Results are reported for the Wine Advisor testbed, a well-known expert system rule base in M.1. Issues in active sampling and learning parameter selection are also discussed
Keywords :
expert systems; learning systems; neural nets; M.1; Wine Advisor; certainty factors; connectionist learning; expert networks; expert system; learning parameter selection; neural network; rule-based knowledge; Artificial intelligence; Computer science; Engines; Expert systems; Intelligent networks; Knowledge acquisition; Logic; Neural networks; System testing; Uncertainty;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170646
