Dialog Learning in Conversational CBR

Mingyang Gu, Agnar Aamodt

Conversational Case-Based Reasoning (CCBR) provides a mixed-initiative dialog for guiding users to refine their problem descriptions incrementally through a question-answering sequence. In this paper, we argue that the successful dialogs in CCBR can be captured and learned in order to improve the efficiency of CCBR from the perspective of shortening the dialog length. A framework for dialog learning in CCBR is proposed in the present paper, and an instance of this framework is implemented and tested empirically in an attempt to evaluate the learning effectiveness of the framework. The results show us that on 29 out of the 32 selected datasets, CCBR with the dialog learning mechanism uses fewer dialog sessions to retrieve the correct case than CCBR without using dialog learning.

Subjects: 3.1 Case-Based Reasoning; 12. Machine Learning and Discovery

Submitted: Feb 13, 2006


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