Qualitative probabilistic networks with reduced ambiguities

  • Authors:

  • Kun Yue;Yu Yao;Jin Li;Wei-Yi Liu

  • Affiliations:

  • Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, People's Republic of China;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, People's Republic of China;Department of Software Engineering, School of Software, Yunnan University, Kunming, People's Republic of China;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, People's Republic of China

  • Venue:
  • Applied Intelligence
  • Year:
  • 2010

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Abstract

A Qualitative probabilistic network (QPN) is the qualitative abstraction of a Bayesian network that encodes variables and the qualitative influences between them. In order to make QPNs be practical for real-world representation and inference of uncertain knowledge, it is desirable to reduce ambiguities in general QPNs, including unknown qualitative influences and inference conflicts. In this paper, we first extend the traditional definition of qualitative influences by adopting the probabilistic threshold. In addition, we introduce probabilistic-rough-set-based weights to the qualitative influences. The enhanced network so obtained, called EQPN, is constructed from sample data. Finally, to achieve conflict-free EQPN inferences, we resolve the trade-offs by addressing the symmetry, transitivity and composition properties. Preliminary experiments verify the correctness and feasibility of our methods.