ABSTRACT
Learning from instruction is a powerful technique for improving problem solving. It is most effective when there is cooperation between the instructor and the student. In one cooperative scenario, the instructor presents examples and partial explanations of them, based on the perceived needs of the student. An active student will predict the instructor's actions and then try to explain the differences from the predictions. This focuses the learning, making it more efficient. We expand the concept of explanation beyond the provably correct explanations of explanation-based learning to include other methods of explanation used by human students. The explanations can use deductions from causal domain knowledge, plausible inferences from the instructor's actions, previous cases of problem solving, and induction. They involve the goal being pursued and the action taken in support of the goal. The explanations result in improved diagnosis and improved future explanation. This combination of explanation techniques leads to more opportunities to learn. We present examples of these ideas from the system we have implemented in the domain of automobile diagnosis.
