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