DocumentCode :
280339
Title :
Artificial intelligence for genomic interpretation
Author :
Sallantin, J. ; Pingand, P.
fYear :
1990
fDate :
33147
Firstpage :
42675
Lastpage :
42678
Abstract :
Biological entities present a complexity level that should be correctly managed in Artificial Intelligence environments. One has to describe and access them in a proper way. What is the role of symbolic learning in this context? The authors define semi-empirical knowledge and theories, which constitute their goals. Knowledge is represented by the means of conceptual graphs, which allow one to manage easily the control of any process of iterative learning to refine the expert´s knowledge. They present an illustration of these principles in a specific domain, protein folding
fLanguage :
English
Publisher :
iet
Conference_Titel :
Symbols Versus Neurons, IEE Colloquium on
Conference_Location :
London
Type :
conf
Filename :
190573
Link To Document :
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