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Machine learning and planning for data management in forestry

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4 Author(s)
S. Matwin ; Ottawa Univ., Ont., Canada ; D. Charlebois ; D. G. Goodenough ; P. Bhogal

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

Published in:

IEEE Expert  (Volume:10 ,  Issue: 6 )