Scientific Research Publishing

Read  full  paper  at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53761#.VNHQqCzQrzE Author(s)  Kun Yue 1* , Feng Wang 2 , Mujin Wei 1 , Weiyi Liu 1   Affiliation(s) 1 Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China . 2 Yunnan Computer Technology Application Key Lab, Kunming University of Science and Technology, Kunming, China . ABSTRACT 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...

Read  full  paper  at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53279#.VLxuU8nQrzE Author(s)    Kun Yue 1 , Feng Wang 2 , Mujin Wei 1 , Weiyi Liu 1   Affiliation(s) 1 Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China . 2 Yunnan Computer Technology Application Key Lab, Kunming University of Science and Technology, Kunming, China . ABSTRACT 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 inf...