NIU Dangdang, LIU Lei, LYU Shuai. Knowledge Compilation Methods Based on the Clausal Relevance and Extension Rule[J]. Chinese Journal of Electronics, 2018, 27(5): 1037-1042. doi: 10.1049/cje.2018.04.006
Citation: NIU Dangdang, LIU Lei, LYU Shuai. Knowledge Compilation Methods Based on the Clausal Relevance and Extension Rule[J]. Chinese Journal of Electronics, 2018, 27(5): 1037-1042. doi: 10.1049/cje.2018.04.006

Knowledge Compilation Methods Based on the Clausal Relevance and Extension Rule

doi: 10.1049/cje.2018.04.006
Funds:  This work is supported by the National Natural Science Foundation of China (No.61300049, No.61502197, No.61503044, No.61763003) and the Natural Science Research Foundation of Jilin Province of China (No.20180101053JC).
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  • Corresponding author: LYU Shuai (corresponding author) was born in 1981. He received the M.S. and Ph.D. degrees in computer software and theory from College of Computer Science and Technology, Jilin University, China in 2007 and 2010, respectively. He is currently an associate professor in College of Computer Science and Technology, Jilin University, China. His research interests include artificial intelligence, automated reasoning and intelligent planning. (Email:lus@jlu.edu.cn)
  • Received Date: 2016-03-01
  • Rev Recd Date: 2016-11-08
  • Publish Date: 2018-09-10
  • We introduce the concepts of Relevancematrix (RM) and Relevance-set (RS). And we construct the association between RM and the Knowledge compilation (KC) methods based on Extension rule (ER). Based on the basic parameters of RS and the relationship between RM and the KC methods based on ER, we design two efficient heuristics, called M2S (maximum sum of elements in RS and sum of literals in RS) and MNE (minimum number of maximum terms not extended by RS). Both of above heuristics intend to find the minimum set of maximum terms which cannot be extended by RS. Furthermore, we apply M2S and MNE on KCER. M2S KCER (KCER with M2S) and MNE KCER (KCER with MNE) are designed and implemented based on M2S and MNE, respectively. Experimentally, for the SAT instances with random lengths of clauses, M2S KCER and MNE KCER can improve the efficiency and quality of KCER sharply, and they are two best KC algorithms of EPCCL (each pair contains complementary literal) theory in all KC algorithms based on KCER.
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