• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Xu Yini, Ouyang Dantong, Liu Meng, Zhang Liming, Zhang Yonggang. Algorithm of Computing Minimal Conflict Sets Based on the Structural Feature of Fault Output[J]. Journal of Computer Research and Development, 2018, 55(11): 2386-2394. DOI: 10.7544/issn1000-1239.2018.20170381
Citation: Xu Yini, Ouyang Dantong, Liu Meng, Zhang Liming, Zhang Yonggang. Algorithm of Computing Minimal Conflict Sets Based on the Structural Feature of Fault Output[J]. Journal of Computer Research and Development, 2018, 55(11): 2386-2394. DOI: 10.7544/issn1000-1239.2018.20170381

Algorithm of Computing Minimal Conflict Sets Based on the Structural Feature of Fault Output

More Information
  • Published Date: October 31, 2018
  • Model-based diagnosis is an important research in the field of artificial intelligence, and the diagnosis based on minimal conflict is a classical method to solve the problem of diagnosis. Therefore computing minimal conflict is an important step in model-based diagnosis. After studying the characteristics of the circuit model, this paper proposes an algorithm of computing minimal conflict sets based on the structural feature of fault output (MCS-SFFO) based on the CSRDSE algorithm. Firstly, the pruning rule of CSRDSE algorithm is improved, and it avoids the access to child leaf nodes of leaf nodes which are not conflict sets. Secondly, the concepts of component set independent of fault output and related to fault output are presented, and the method of computing component set independent of fault output is provided according to the system description and observation. Finally, a theorem about non-conflict set is proposed, that is, the component set independent of fault output and its subset are not conflict sets. MCS-SFFO algorithm that computes minimal conflict set is presented according to the non-conflict set theorem. Compared with CSRDSE algorithm, MCS-SFFO algorithm further prunes the non-solution space and reduces the number of calls to the SAT solver. Experimental evidence indicates that MCS-SFFO algorithm has better computational efficiency than CSRDSE algorithm.
  • Related Articles

    [1]Hu Yunshu, Zhou Jun, Cao Zhenfu, Dong Xiaolei. Lightweight Multi-User Verifiable Privacy-Preserving Gene Sequence Analysis Scheme[J]. Journal of Computer Research and Development, 2024, 61(10): 2448-2466. DOI: 10.7544/issn1000-1239.202440453
    [2]Wang Chenxu, Cheng Jiacheng, Sang Xinxin, Li Guodong, Guan Xiaohong. Data Privacy-Preserving for Blockchain: State of the Art and Trends[J]. Journal of Computer Research and Development, 2021, 58(10): 2099-2119. DOI: 10.7544/issn1000-1239.2021.20210804
    [3]Song Xiangfu, Gai Min, Zhao Shengnan, Jiang Han. Privacy-Preserving Statistics Protocol for Set-Based Computation[J]. Journal of Computer Research and Development, 2020, 57(10): 2221-2231. DOI: 10.7544/issn1000-1239.2020.20200444
    [4]Zhou Jun, Shen Huajie, Lin Zhongyun, Cao Zhenfu, Dong Xiaolei. Research Advances on Privacy Preserving in Edge Computing[J]. Journal of Computer Research and Development, 2020, 57(10): 2027-2051. DOI: 10.7544/issn1000-1239.2020.20200614
    [5]Liu Junxu, Meng Xiaofeng. Survey on Privacy-Preserving Machine Learning[J]. Journal of Computer Research and Development, 2020, 57(2): 346-362. DOI: 10.7544/issn1000-1239.2020.20190455
    [6]Song Lei, Ma Chunguang, Duan Guanghan, Yuan Qi. Privacy-Preserving Logistic Regression on Vertically Partitioned Data[J]. Journal of Computer Research and Development, 2019, 56(10): 2243-2249. DOI: 10.7544/issn1000-1239.2019.20190414
    [7]Zhou Jun, Dong Xiaolei, Cao Zhenfu. Research Advances on Privacy Preserving in Recommender Systems[J]. Journal of Computer Research and Development, 2019, 56(10): 2033-2048. DOI: 10.7544/issn1000-1239.2019.20190541
    [8]Zhu Liehuang, Gao Feng, Shen Meng, Li Yandong, Zheng Baokun, Mao Hongliang, Wu Zhen. Survey on Privacy Preserving Techniques for Blockchain Technology[J]. Journal of Computer Research and Development, 2017, 54(10): 2170-2186. DOI: 10.7544/issn1000-1239.2017.20170471
    [9]Fang Weiwei, Ren Jiang, Xia Hongke. Heterogeneous Distributed Linear Regression Privacy-Preserving Modeling[J]. Journal of Computer Research and Development, 2011, 48(9): 1685-1692.
    [10]Zhang Zhancheng, Wang Shitong, Fu-Lai Chung. Collaborative Classification Mechanism for Privacy-Preserving[J]. Journal of Computer Research and Development, 2011, 48(6): 1018-1028.

Catalog

    Article views (1011) PDF downloads (370) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return