Dialogue Learning in CCBR

Abstract

In the field of palliative care there is a need to create adaptive questionnaires to minimize the patient's "cognitive load" when acquiring data of the patient's subjective experience of pain. A conversational case based reasoning (CCBR) system can be used as a basis for such questionnaires, and dialogue learning as a method for reducing the number of questions asked, without deterioration of the data quality. In this thesis, methods for question ranking, dialogue inferring, and dialogue learning have been reviewed. A case based reasoning framework is introduced and improved, and based on this, a CCBR system with an extension for dialogue learning has been designed and implemented. The result was tested with well known datasets, as well as new data from a survey on patients' experience of pain. Evaluation shows that dialogue learning can be used to reduce the number of questions asked, but also reveals some problems when it comes to automatically evaluation of solutions found using query biased similarity measures.