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.
Saung Hnin Pwint OO
Thammasat University
Nguyen Duy HUNG
Thammasat University
Thanaruk THEERAMUNKONG
Thammasat University
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Saung Hnin Pwint OO, Nguyen Duy HUNG, Thanaruk THEERAMUNKONG, "Knowledge Integration by Probabilistic Argumentation" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 8, pp. 1843-1855, August 2020, doi: 10.1587/transinf.2019EDP7270.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7270/_p
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@ARTICLE{e103-d_8_1843,
author={Saung Hnin Pwint OO, Nguyen Duy HUNG, Thanaruk THEERAMUNKONG, },
journal={IEICE TRANSACTIONS on Information},
title={Knowledge Integration by Probabilistic Argumentation},
year={2020},
volume={E103-D},
number={8},
pages={1843-1855},
abstract={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.},
keywords={},
doi={10.1587/transinf.2019EDP7270},
ISSN={1745-1361},
month={August},}
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TY - JOUR
TI - Knowledge Integration by Probabilistic Argumentation
T2 - IEICE TRANSACTIONS on Information
SP - 1843
EP - 1855
AU - Saung Hnin Pwint OO
AU - Nguyen Duy HUNG
AU - Thanaruk THEERAMUNKONG
PY - 2020
DO - 10.1587/transinf.2019EDP7270
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2020
AB - 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.
ER -