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

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 e-cient 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 e-cient two-step inference algorithm consisting of (a) logic operation to flnd 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.