Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
A Knowledge-Intensive Method for Conversational Mingyang Gu and Agnar Aamodt
 

Summary: A Knowledge-Intensive Method for Conversational
CBR
Mingyang Gu and Agnar Aamodt
Department of Computer and Information Science, Norwegian University of Science and
Technology, Sem Sælands vei 7-9, N-7491, Trondheim, Norway
{Mingyang.Gu, Agnar.Aamodt}@idi.ntnu.no
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.
1 Introduction

  

Source: Aamodt, Agnar - Department of Computer and Information Science, Norwegian University of Science and Technology

 

Collections: Computer Technologies and Information Sciences