Belief Merging for Possibilistic Belief Bases

被引：0

作者：

Thi Thanh Luu Le ^{[1
,2
]}

Trong Hieu Tran ^{[2
]}

机构：

[1] Univ Finance & Accountancy, Quangngai, Vietnam

[2] VNU Univ Engn & Technol, Hanoi, Vietnam

来源：

ADVANCED COMPUTATIONAL METHODS FOR KNOWLEDGE ENGINEERING (ICCSAMA 2019) | 2020年 / 1121卷

关键词：

Belief merging; Possibilistic logic; Prioritized belief base; KNOWLEDGE BASES; FUSION;

D O I：

10.1007/978-3-030-38364-0_33

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Belief merging has received much attention from the research community with a large range of applications in Computer Science and Artificial Inteligence. In this paper, we represent a new belief merging approach for prioritized belief bases. The main idea of this method is to use two operators, namely connective strong operator and averagely increasing operator to merge possibilistic belief bases. By this way, the proposed method allows to keep more useful beliefs, which may be lost in other methods because of drowning effect. The logical properties of merging result are also analyzed and discussed.

引用

页码：370 / 380

页数：11

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