While stable matching problems are widely studied, little work has investigated schemes for effectively eliciting agent preferences using either preference (e.g., comparison) queries for interviews (to form such comparisons); and no work has addressed how to combine both. We develop a new model for representing and assessing agent preferences that accommodates both forms of information and (heuristically) minimizes the number of queries and interviews required to determine a stable matching. Our Refine-then-Interview (RtI) scheme uses coarse preference queries to refine knowledge of agent preferences and relies on interviews only to assess comparisons of relatively “close” options. Empirical results show that RtI compares favorably to a recent pure interview minimization algorithm, and that the number of interviews it requires is generally independent of the size of the market.

We explore the frequency and impact of misunderstandings in an existing corpus of tutorial dialogues in which a student appears to get an interpretation that is not in line with what the system developers intended. We found that this type of error is frequent, regardless of whether student input is typed or spoken, and that it does not respond well to general misconception repair strategies. Further we found that it is feasible to detect misunderstandings and suggest alternative strategies for repairing them that we intend to test in the future.