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Merging Possibilistic Belief Bases by Argumentation

Nguyen, Thi Hong Khanh and Tran, Trong Hieu and Nguyen, Tran Van and Le, Thi Thanh Luu (2017) Merging Possibilistic Belief Bases by Argumentation. In: Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Kanazawa, Japan, April 3-5, 2017, Proceedings, Part I.

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Belief merging is one of active research fields with a large range of applications in Artificial Intelligence. Most of the work in this research field is in the centralized approach, however, it is difficult to apply to interactive systems such as multi-agent systems. In this paper, we introduce a new argumentation framework for belief merging. To this end, a constructive model to merge possiblistic belief bases built based on the famous general argumentation framework is proposed. An axiomatic model, including a set of rational and intuitive postulates to characterize the merging result is introduced and several logical properties are mentioned and discussed.

Item Type: Conference or Workshop Item (Paper)
Subjects: Electronics and Communications > Electronics and Computer Engineering
Divisions: Faculty of Information Technology (FIT)
Depositing User: Hà Quang Thụy
Date Deposited: 29 Dec 2017 08:50
Last Modified: 29 Dec 2017 08:50

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