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
Link To Document :
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