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Approximate inference for assumption-based argumentation in AI

  • This thesis focuses on approximate inference in assumption-based argumentation frameworks. Argumentation provides a significant idea in the computerization of theoretical and practical reasoning in AI. And it has a close connection with AI, engaging in arguments to perform scientific reasoning. The fundamental approach in this field is abstract argumentation frameworks developed by Dung. Assumption-based argumentation can be regarded as an instance of abstract argumentation with structured arguments. When facing a large scale of data, a challenge of reasoning in assumption-based argumentation is how to construct arguments and resolve attacks over a given claim with minimal cost of computation and acceptable accuracy at the same time. This thesis proposes and investigates approximate methods that randomly select and construct samples of frameworks based on graphical dispute derivations to solve this problem. The presented approach aims to improve reasoning performance and get an acceptable trade-off between computational time and accuracy. The evaluation shows that for reasoning in assumption-based argumentation, in general, the running time is reduced with the cost of slightly low accuracy by randomly sampling and constructing inference rules for potential arguments over a query.

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Metadaten
Author:Chuyi Sun
URN:urn:nbn:de:kola-21834
Referee:Matthias Thimm, Tjitze Rienstra
Document Type:Master's Thesis
Language:English
Date of completion:2021/03/31
Date of publication:2021/04/01
Publishing institution:Universität Koblenz-Landau, Universitätsbibliothek
Granting institution:Universität Koblenz-Landau, Campus Koblenz, Fachbereich 4
Date of final exam:2021/04/15
Release Date:2021/04/01
Number of pages:ix, 76 Seiten
Institutes:Fachbereich 4 / Institute for Web Science and Technologies
Licence (German):License LogoEs gilt das deutsche Urheberrecht: § 53 UrhG