Rough-set based approach to solve the inference conflict in qualitative probabilistic network
基于粗糙集的定性概率网推理冲突解决方法

Qualitative Probabilistic Networks (QPNs) are the qualitative abstraction of Bayesian networks by substituting the conditional probabilistic parameters by qualitative influences on directed edges. Efficient algorithms have been developed for QPN reasoning. Due to the high abstraction, unresolved trade-offs (i.e., conflicts) during inferences with qualitative probabilistic networks may be produced. Motivated by avoiding the conflicts of QPN reasoning, a rough-set-theory based approach was proposed. The attribute association degrees between node peers were calculated based on the rough-set-theory while the QPNs were constructed. The association degrees were adopted as the weights to solve the conflicts during QPN inferences. Accordingly, the algorithm of QPN reasoning was improved by incorporating the attribute association degrees. By applying this method, the efficiency of QPNs inferences can be preserved, and the inference conflict can be well addressed at the same time.