Can users infer missing information in conversational case-based reasoning: the effects of providing context

Abstract

Fault diagnosis can be supported by conversational case-based reasoning, but case descriptions are often incomplete. However, users might be able to infer missing information when the system provides context information. We investigated how such information affects ratings, solution times, and learning. Using a simulated case-based reasoning system, participants could ask about the presence of symptoms to find out how well each case matched the current situation. The system answered with yes or no, simply stated that it did not know the answer, or provided three or 15 pieces of context information (suggesting similarity, dissimilarity, or including no relevant information). Only context suggesting similarity increased accuracy, while all types of context increased subjective certainty. The amount of context had little impact: a high amount decreased speed and learning, but much less than expected. Taken together, context information can be helpful, but performance outcomes depend on the type of context provided.