1 March 1994 Case-based reasoning in an intelligent information system for forestry
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Abstract
Our objective is to integrate transformational analogy, derivational analogy, and goal- regression to create solutions for an intelligent system called SEIDAM (System of Experts for Intelligent Data Management). SEIDAM answers queries about forests and the environment through the integration of remote sensing, geographic information, models, and field measurements. A query (problem) could require, for example, that a forest inventory stored in a geographical information system be updated to reflect past harvesting by overlaying current satellite imagery over forest cover maps. A case consists of a query, remote sensing data, and geographic information, and the analysis methods to answer the query. SEIDAM will consist of approximately 150 expert systems performing satellite and aircraft image analysis, integrated to multiple GIS and a relational database. Derivational analogy provides the means by which this search can be expanded knowledgeably; i.e., provide a knowledge-based approach justifying the expansion of the search. Transformational analogy eliminates the problems associated with searching by foregoing a search altogether. The advantage is that the intractability of exploring the search space is no longer a consideration.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Daniel Charlebois, Daniel Charlebois, David G. Goodenough, David G. Goodenough, Stan Matwin, Stan Matwin, } "Case-based reasoning in an intelligent information system for forestry", Proc. SPIE 2244, Knowledge-Based Artificial Intelligence Systems in Aerospace and Industry, (1 March 1994); doi: 10.1117/12.169408; https://doi.org/10.1117/12.169408
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