Journal of Guangxi Normal University(Natural Science Edition) ›› 2021, Vol. 39 ›› Issue (4): 47-54.doi: 10.16088/j.issn.1001-6600.2020082701

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An OBDD Construction Method for Knowledge Compilation

WANG Jinyan1,2*, HU Chun2, GAO Jian3   

  1. 1. Guangxi Key Lab of Multisource Information Mining and Security (Guangxi Normal University), Guilin Guangxi 541004, China;
    2. School of Computer Science and Engineering, Guangxi Normal University, Guilin Guangxi 541004, China;
    3. School of Information Science and Technology, Dalian Maritime University, Dalian Liaoning 116026, China
  • Received:2020-08-27 Revised:2020-11-18 Online:2021-07-25 Published:2021-07-23

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Abstract: Knowledge compilation, as a key direction of artificial intelligence, plays an important role in on-line requirement and reasoning. The ordered binary decision diagram (OBDD) is a main target language in knowledge compilation, which has been widely used to compile many real-world satisfiability (SAT) problems. The construction algorithms for OBDDs are deeply studied in the past years, and the purposes are to decrease the size of target OBDD and shorten the compilation time. The construction for OBDD is an important factor affecting the efficiency of compilation. To improve the time efficiency, an improved construction algorithm for OBDDs is proposed. The algorithm compiles the causes in SAT problem into OBDDs and then combines them into a whole OBDD. Different from the traditional algorithm which combines these OBDDs into a target OBDD one by one, this approach first combines some OBDDs into larger OBDDs and then they are merged into a garget OBDD. The experiments on randomly generated SAT instances and configuration problems indicate that the proposed OBDD construction algorithm performs better than the original algorithm.

Key words: artificial intelligence, knowledge compilation, ordered binary decision diagram, satisfiability problem, off-line preprocessing, on-line reasoning

CLC Number: 

  • TP301
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