A knowledge-intensive method for conversational CBR

  • Authors:

  • Mingyang Gu;Agnar Aamodt

  • Affiliations:

  • Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway;Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway

  • Venue:
  • ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
  • Year:
  • 2005

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Abstract

In conversational case-based reasoning (CCBR), a main problem is how to select the most discriminative questions and display them to users in a natural way to alleviate users' cognitive load. This is referred to as the question selection task. Current question selection methods are knowledge-poor, that is, only statistical metrics are taken into account. In this paper, we identify four computational tasks of a conversation process: feature inferencing, question ranking, consistent question clustering and coherent question sequencing. We show how general domain knowledge is able to improve these processes. A knowledge representation system suitable for capturing both cases and general knowledge has been extended with meta-level relations for controlling a CCBR process. An “explanation-boosted” reasoning approach, designed to accomplish the knowledge-intensive question selection tasks, is presented. An application of our implemented system is illustrated in the car fault detection domain.