An Approach for Generalising Symbolic Knowledge

  • Richard Dazeley
  • Byeong-Ho Kang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5360)

Abstract

Many researchers and developers of knowledge based systems (KBS) have been incorporating the notion of context. However, they generally treat context as a static entity, neglecting many connectionists’ work in learning hidden and dynamic contexts, which aids generalization. This paper presents a method that models hidden context within a symbolic domain achieving a level of generalisation. Results indicate that the method can learn the information that experts have difficulty providing by generalising the captured knowledge.

Keywords

hidden context knowledge based systems knowledge representation ripple-down rules situation cognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Richard Dazeley
    • 1
  • Byeong-Ho Kang
    • 2
  1. 1.School of Information Technology and Mathematical SciencesUniversity of BallaratBallaratAustralia
  2. 2.School of Computing and Information SystemsUniversity of TasmaniaHobartAustralia

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