Learning to Ask the Right Questions to Help a Learner Learn

Abstract

Intelligent systems require substantial bodies of problem-solving knowledge. Machine learning techniques hold much appeal for acquiring such knowledge but typically require extensive amounts of user-supplied training data. Alternatively, informed question asking can supplement machine learning by directly eliciting critical knowledge from a user. Question asking can reduce the amount of training data required, and hence the burden on the user; furthermore, focused question asking holds significant promise for faster and more accurate acquisition of knowledge. In previous work, we developed static strategies for question asking that provide background knowledge for a base learner, enabling the learner to make useful generalizations even with few training examples. Here, we extend that work with a learning approach for automatically acquiring question-asking strategies that better accommodate the interdependent nature of questions. We present experiments validating the approach and showing its usefulness for acquiring efficient, context-dependent question-asking strategies.