• DocumentCode
    2567896
  • Title

    MCN and MO: Two Heuristic Strategies in Knowledge Compilation Using Extension Rule

  • Author

    Wang, Jinyan ; Gu, Wenxiang ; Yin, Minghao ; Wang, Dongxiu

  • Author_Institution
    Sch. of Math. & Stat., Northeast Normal Univ., Changchun, China
  • fYear
    2009
  • fDate
    15-17 May 2009
  • Firstpage
    389
  • Lastpage
    393
  • Abstract
    EPCCL theory is a target language in knowledge compilation. Knowledge compilation using extension rule (KCER) is a method for knowledge compilation and takes EPCCL theory as target language. Since the size of the compiled knowledge base directly influences the efficiency of on-line queries, in this paper, we present two heuristic strategies aimed at minimizing the size of the compiled knowledge base. They are MCN and MO applied respectively to choose clause and variable. Experimental results show that the two heuristic strategies play a great role in minimizing the size of the compiled knowledge base. The sizes gained by adding MCN and MO to KCER are 3.0-32.9 times less than the sizes gained by KCER without any heuristic.
  • Keywords
    inference mechanisms; knowledge based systems; query processing; compiled knowledge base; extension rule; heuristic strategies; knowledge compilation; target language; Boolean functions; Data structures; Heuristic algorithms; Mathematics; Polynomials; Signal processing; Signal processing algorithms; Statistics; Sun; extension rule; heuristic strategy; knowledge compilation; target language;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    2009 International Conference on Signal Processing Systems
  • Conference_Location
    Singapore
  • Print_ISBN
    978-0-7695-3654-5
  • Type

    conf

  • DOI
    10.1109/ICSPS.2009.99
  • Filename
    5166814