Volume 3,Issue 7
立体因果图中的条件作用事件
着眼于拓展条件作用事件在动态因果推理中的应用范围,进一步完善立体因果图(Cubic DUCG)的理论架构,重点研究了穿越式因果连接中的两种新形式—— 条件作用延展与条件作用延滞。通过建立包含此类事件的立体因果图模型,文章分析了它们在约简前后的逻辑结构对应关系,证明了二者在动态推理体系中具有逻辑等价性。结果表明,一般动态不确定因果图(DUCG)中关于条件作用事件的已有结论能够有效地迁移至立体因果图。这一研究工作不仅丰富了立体因果图的理论内涵,也为处理具有时序依赖与结构复杂性的不确定因果问题提供了更为系统、高效的理论基础与分析工具。
[1] PEARL J. Fusion, propagation and structuring in belief networks[J]. Artificial Intelligence, 1986, 29(3): 241-288.
[2] MIROSLAV S, JANA M, DIMITRIS D. Bayesian network application for the risk assessment of existing energy production units[J]. Pathology and Immunopathology Research, 2018, 169: 312-320.
[3] ZHANG D, LIU Q, YAN H, et al. A matrix analytic approach for Bayesian network modeling and inference of a manufacturing system[J]. Acta Oceanological Sinica, 2021,60: 202-213.
[4] 张勤. DUCG: 一种新的动态不确定因果知识的表达和推理方法( Ⅰ ): 离散、静态、证据确定和有向无环图情况[J]. 计算机学报, 2010, 33(4): 625-651.
[5] ZHANG Q, DONG C, CUI Y. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: statistics base, matrix, and application[J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(4): 645-663.
[6] ZHANG Q. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: directed cyclic graph and joint probability distribution[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(7): 1503-1517.
[7] Zhang Q. Dynamic uncertain causality graph for knowledge representation and reasoning: continuous variable, uncertain evidence, and failure forecast[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 45(7): 990-1003.
[8] ZHANG Q, GENG S. Dynamic uncertain causality graph applied to dynamic fault diagnoses of large and complex systems[J]. IEEE Transactions on Reliability, 2015, 64(3): 910-927.
[9] 李晓宾, 李淑珍. 模糊动态贝叶斯网络防御态势感知模型[J]. 探测与控制学报, 2017, 39(3): 124-129.
[10] 董春玲, 赵越, 张勤. 动态故障诊断中的立体因果建模与不确定性推理方法[J]. 清华大学学报 ( 自然科学版), 2018, 58(7): 614-622.
[11] 胡婷婷, 王洪春, 吴成姚. 基于M-DUCG 中条件作用事件展开的一般形式[J]. 重庆师范大学学报( 自然科学版),2019,36(02):75-80.