Explanation-Boosted Question Selection in Conversational CBR

A core research concern in conversational case-based reasoning (CCBR) is how to select the most discriminative and natural questions to ask the user in the conversational process. There are two ways to realize this task: one is to remove the questions whose answers can be inferred from the information a user has provided, which is called dialogue inferencing; the other is to rank the questions to guarantee the most informative questions are asked first, which is referred to as question ranking. In this paper, we present a common explanation-boosted CCBR approach, which utilizes both general domain knowledge and case-specific knowledge to realize dialogue inferencing and question ranking. This approach provides a flexible meta-level knowledge representation model to be able to incorporate richer semantic relations. An application of this approach is illustrated in a car fault detection domain.