Ordered proposition fusion based on consistency and uncertainty measurements

• Published in: Science China. Information sciences 2017-08, Vol.60 (8), p.113-131, Article 082103
• Main Authors: Liu, Dayou, Zhu, Yungang, Ni, Ni, Liu, Jie
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Computer Science; Consistency; Convexity; Fertility; Information Systems and Communication Service; Research Paper; Roles; Uncertainty; 一致性; 不确定性度量; 人工智能; 命题; 支持函数; 支撑函数; 有序; 融合方法
• ISSN: 1674-733X; 1869-1919
• DOI: 10.1007/s11432-016-9101-8

The fusion of ordered propositions is an important and widespread problem in artificial intelligence,but existing fusion methods have difficulty handling the fusion of ordered propositions. In this paper, we propose a solution based on consistency and uncertainty measurements. The main contributions of this paper are as follows. First, we propose the concept of convexity degree, mean, and center of basic support function to comprehensively describe the basic support function of ordered propositions. Second, we introduce entropy as a measure of uncertainty in the basic support function of ordered propositions. Third, we generalize the indeterminacy of the basic support function and propose a novel method to measure the consistency between two basic support functions. Finally, based on the above researches, we propose a novel algorithm for fusing ordered propositions. Theoretical analysis and experimental results demonstrate that the proposed method outperforms state-of-the-art methods.