Toward an expert system for identification of minerals in thin section

Minerals may be identified by optical inspection and x-ray diffraction analysis. Full and correct identification, however, requires experience and extensive knowledge of mineral characteristics and association. Computer systems designed to approach levels of human expertise in similarly complex identification tasks have become increasingly effective with the application and refinement of various Artificial Intelligence (AI) techniques. These knowledge-based systemsuse the skills, knowledge, and rules of thumb that distinguish the expert from the knowledgeable layman to emulate human expertise. They also may be modified to serve a tutorial role whereby a nonexpert's approach to the task may be compared with that of the expert (system), and criticized accordingly. Such a knowledge-based system capable of identifying minerals from their optical characteristics is being developed at the University of Reading.