Tangent Recognition and Anomaly Pruning to TRAP Off-Topic Questions in Conversational Case-Based Dialogues

摘要

In any knowledge investigation by which a user must acquire new or missing information, situations often arise which lead to a fork in their investigation. Multiple possible lines of inquiry appear that the users must choose between. A choice of any one would delay the user's ability to choose another, if the chosen path proves to be irrelevant and happens to yield only useless information. With limited knowledge or experience, a user must make assumptions which serve as justifications for their choice of a particular path of inquiry. Yet incorrect assumptions can lead the user to choose a path that ultimately leads to dead-end. These fruitless lines of inquiry can waste both time and resources by adding confusion and noise to the user's investigation. Here we evaluate an algorithm called Tangent Recognition Anomaly Pruning to eliminate false starts that arise in interactive dialogues created within our case-based reasoning system called Ronin. Results show that Tangent Recognition Anomaly Pruning is an effective algorithm for processing mistakes when reusin case reuse.