Machine learning and planning for data management in forestry

Author

Matwin, Stan ; Charlebois, Daniel ; Goodenough, David G. ; Bhogal, Pal

Author_Institution

Ottawa Univ., Ont., Canada

Volume

10

Issue

6

fYear

1995

fDate

12/1/1995 12:00:00 AM

Firstpage

35

Lastpage

41

Abstract

The Seidam project uses an AI planning-based approach that combines three problem-solving methods-transformational analogy, derivational analogy and goal regression-to automatically answer forest-management queries. The project is conducted under NASA´s Applied Information Systems Research Program. Seidam, which runs on a Sun Sparcstation using the Solaris 2.3 version of Unix, is a complex system that relies on extensive cooperation between expert systems and processing agents

Keywords

deductive databases; forestry; learning (artificial intelligence); planning (artificial intelligence); query processing; software agents; AI planning; Applied Information Systems Research Program; NASA; Palermo; Seidam project; Solaris 2.3; Sun Sparcstation; Unix; automatic query answering; data management; derivational analogy; expert systems; forest-management queries; forestry; goal regression; machine learning; problem-solving methods; processing agents; transformational analogy; Environmental management; Forestry; Geographic Information Systems; Image analysis; Machine learning; Problem-solving; Project management; Resource management; Satellites; Software development management;

fLanguage

English

Journal_Title

IEEE Expert

Publisher

ieee

ISSN

0885-9000

Type

jour

DOI

10.1109/64.483115

Filename

483115