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A distance-based approach for merging probabilistic knowledge bases

Nguyen, Van Tham and Nguyen, Ngoc Thanh and Tran, Trong Hieu (2019) A distance-based approach for merging probabilistic knowledge bases. Journal of Intelligent & Fuzzy Systems . pp. 1-14. ISSN 10641246

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In the stages of development of probabilistic expert systems, knowledge merging is a major concern. To deal with knowledge merging problems, several approaches have been put forward. However, in the proposed models, each original probabilistic knowledge base (PKB) is represented by a set of probabilistic functions fulfilling such knowledge base. The drawbacks of the solutions are that the output of model is also a set of probabilistic functions satisfying the resulting PKB and there is no algorithm for implementing the merging process of PKBs in which each of them consists of probabilistic constraints. In this paper, distance-based approach is utilized to propose a new method of merging PKBs to ensure that both the input and output of methods are represented by sets of probabilistic constraints. To this aim, the relationship between the probability rules and the probabilistic constraints, and the several transformation methods for the representation of the original PKB are presented, a set of merging operators (MOs) is proposed, and several desirable logical properties are investigated and discussed. Several algorithms for merging PKBs are presented and the computational complexities of these algorithms are also analyzed and evaluated.

Item Type: Article
Subjects: Information Technology (IT)
ISI-indexed journals
Divisions: Advanced Insitute of Engineering and Technology (AVITECH)
Faculty of Information Technology (FIT)
Depositing User: Dr. Trọng Hiếu Trần
Date Deposited: 29 Nov 2019 03:37
Last Modified: 29 Nov 2019 03:37

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