期刊名称：International Journal of Intelligence Science
印刷版ISSN：2163-0283
电子版ISSN：2163-0356
出版年度：2015
卷号：05
期号：03
页码：133-144
DOI：10.4236/ijis.2015.53012
语种：English
出版社：Scientific Research Publishing
摘要：Bayesian network (BN) is a well-accepted framework for representing and inferring uncertain knowledge. As the qualitative abstraction of BN, qualitative probabilistic network (QPN) is introduced for probabilistic inferences in a qualitative way. With much higher efficiency of inferences, QPNs are more suitable for real-time applications than BNs. However, the high abstraction level brings some inference conflicts and tends to pose a major obstacle to their applications. In order to eliminate the inference conflicts of QPN, in this paper, we begin by extending the QPN by adding a mutual-information-based weight (MI weight) to each qualitative influence in the QPN. The extended QPN is called MI-QPN. After obtaining the MI weights from the corresponding BN, we discuss the symmetry, transitivity and composition properties of the qualitative influences. Then we extend the general inference algorithm to implement the conflict-free inferences of MI-QPN. The feasibility of our method is verified by the results of the experiment.
