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Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Discrete DAG Cases

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Abstract

Developed from the dynamic causality diagram (DCD) model, a new approach for knowledge representation and reasoning named as dynamic uncertain causality graph (DUCG) is presented, which focuses on the compact representation of complex uncertain causalities and efficient probabilistic inference. It is pointed out that the existing models of compact representation and inference in Bayesian Network (BN) is applicable in single-valued cases, but may not be suitable to be applied in multi-valued cases. DUCG overcomes this problem and beyond. The main features of DUCG are: 1) compactly and graphically representing complex conditional probability distributions (CPDs), regardless of whether the cases are single-valued or multi-valued; 2) able to perform exact reasoning in the case of the incomplete knowledge representation; 3) simplifying the graphical knowledge base conditional on observations before other calculations, so that the scale and complexity of problem can be reduced exponentially; 4) the efficient two-step inference algorithm consisting of (a) logic operation to find all possible hypotheses in concern for given observations and (b) the probability calculation for these hypotheses; and 5) much less relying on the parameter accuracy. An alarm system example is provided to illustrate the DUCG methodology.

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Authors and Affiliations

  1. School of Computer Science and Technology, Beihang University, Beijing, 100191, China

    Qin Zhang

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  1. Qin Zhang
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Correspondence to Qin Zhang.

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This work is supported by Guangdong Nuclear Power Group of China under Contract No. CNPRI-ST10P005 and the National Natural Science Foundation of China under Grant No. 60643006.

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Zhang, Q. Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Discrete DAG Cases. J. Comput. Sci. Technol. 27, 1–23 (2012). https://doi.org/10.1007/s11390-012-1202-7

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  • DOI: https://doi.org/10.1007/s11390-012-1202-7

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