Automated knowledge generation in support of Shuttle ground operations

A system is described that performs automated knowledge generation (AKG) using structural databases to create semantic knowledge bases. The approach, borrowed from machine vision, uses relaxation labeling to propagate constraints through the system. A prototype system has been developed which accesses an Intergraph CAD database, `processes´ the information contained therein, and generates a frame-based knowledge base usable by NASA´s Knowledge-based Autonomous Test Engineer (KATE) diagnostic system. The authors present a summary of work to date and discuss some of the obstacles found by the research group during the development of the AKG system