1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
3. Beijing Entry-Exit Inspection and Quarantine Bureau, Beijing 100026, China
Corresponding author (email@example.com
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.
Explanation of negative regulation for basic support function.
$\omega \leftarrow \gamma$;
ELSIF $\mu \in \Delta$ AND $\nu \notin \Delta$ $\omega \leftarrow \nu$; ELSIF $\mu \notin \Delta$ AND $\nu \in \Delta$ $\omega \leftarrow \mu$;
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