Abstract
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.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61133011, 61502198, 61572226, 61472161, 61373053), China Postdoctoral Science Foundation (Grant No. 2013M541303), Science and Technology Development Program of Jilin Province of China (Grant No. 20150520066-JH), and State Key Laboratory of Applied Optics. The authors would like to appreciate the handling editor and the anonymous reviewers, whose constructive and insightful comments greatly helped in improving this paper.
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Liu, D., Zhu, Y., Ni, N. et al. Ordered proposition fusion based on consistency and uncertainty measurements. Sci. China Inf. Sci. 60, 082103 (2017). https://doi.org/10.1007/s11432-016-9101-8
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DOI: https://doi.org/10.1007/s11432-016-9101-8