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Knowledge Integration by Probabilistic Argumentation

Saung Hnin Pwint OO, Nguyen Duy HUNG, Thanaruk THEERAMUNKONG

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Summary :

While existing inference engines solved real world problems using probabilistic knowledge representation, one challenging task is to efficiently utilize the representation under a situation of uncertainty during conflict resolution. This paper presents a new approach to straightforwardly combine a rule-based system (RB) with a probabilistic graphical inference framework, i.e., naïve Bayesian network (BN), towards probabilistic argumentation via a so-called probabilistic assumption-based argumentation (PABA) framework. A rule-based system (RB) formalizes its rules into defeasible logic under the assumption-based argumentation (ABA) framework while the Bayesian network (BN) provides probabilistic reasoning. By knowledge integration, while the former provides a solid testbed for inference, the latter helps the former to solve persistent conflicts by setting an acceptance threshold. By experiments, effectiveness of this approach on conflict resolution is shown via an example of liver disorder diagnosis.

Publication
IEICE TRANSACTIONS on Information Vol.E103-D No.8 pp.1843-1855
Publication Date
2020/08/01
Publicized
2020/05/01
Online ISSN
1745-1361
DOI
10.1587/transinf.2019EDP7270
Type of Manuscript
PAPER
Category
Artificial Intelligence, Data Mining

Authors

Saung Hnin Pwint OO
  Thammasat University
Nguyen Duy HUNG
  Thammasat University
Thanaruk THEERAMUNKONG
  Thammasat University

Keyword