Case-based reasoning in an intelligent information system for forestry

摘要

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.