Reasoning Algorithm in Multi-Value Causality Diagram
Fan Xing · Chinese Journal of Computers · 2003
The multi value causality diagram developed on the belief network does not satisfy probability theory rigorous, and the inference result may be error when it is used in practice. In order to overcome these difficulties, this paper presents a reasoning algorithm based on possibility allocation. The reasoning process is separates into 3 stages. Firstly, the multi value causality diagram is supplementally defined. It is compatible with a single value causality diagram. Secondly, it transforms a multi value causality diagram to a single value causality diagram that is used to compute the probability; Thirdly, it allocate the probability to every state according to its possibility value that is computed in multi value causality diagram. An example about fault diagnosis of a steam generator in the nuclear power plant demonstrates that this algorithm could overcome efficiently the difficulties in multi value diagram, the reasoning process is rigorous, and the result coincides with the reality. Aimed to the former algorithm's deficiency, e.g. it cannot deal with the fuzzy case, a fuzzy reasoning algorithm is presented in this paper, which extended the definition of the multi value causality diagram with fuzzy, builts the fuzzy mapping relation between the event variable and the reader variable that to represent the fuzzy knowledge, and defines a suppositional equivalent fuzzy state of event variable that maps the reader variable to a fuzzy state and transforms the fuzzy reasoning of a reader variable to the non fuzzy reasoning of a fuzzy state. Now, the causality diagram has become a hybrid probability knowledge representation and reasoning model, which can deal with discrete and continuous variables and represent the fuzzy knowledge under uncertainty.