The use of explanation-based learning as part of a larger knowledge compilation system for automating the development and maintenance of associate knowledge bases is discussed. Specifically, the implemented systems of the Learning Systems for Pilot Aiding (LSPA), which automates portions of the offline process of incorporating new information into the knowledge bases of Pilot's Associate, one of the largest and most thoroughly developed associate systems, and then propagates pertinent changes to other Pilot's Associate modules, are described. The learning algorithm and some modifications to it are also described. It is shown that the general approach should be relevant and easily generalizable to other intelligent associate systems, such as a submarine commander's associate and a helicopter pilot's associate. For substantially different systems, explanation-based learning should be generally valid as a front end for knowledge compilation