Linked-learning for knowledge acquisition: a pilot's associate case study

We developed a knowledge acquisition system that uses an Explanation-Based Learning domain theory as a knowledge repository from which general knowledge structures can be compiled and then translated by smart translators into the various specialized representations required for the separate expert system modules of a distributed pilot aiding system. We call this two-stage learning-plus-translation process linked learning. This architecture addresses learning for multiple modules with different knowledge representations and performance goals, but which must all perform together in an integrated fashion. It also addresses learning for an intelligent agent which must perform in a real-world, dynamically-changing environment with multiple sources of uncertainty. Finally, it serves as a case study offering insights into the integration of machine learning into the system engineering process for a large knowledge-based system development effort.