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Composite Argumentation Systems with ML Components
Nguyen Duy Hung, Nam-Van Huynh, Thanaruk Theeramunkong, Tho-Quy Nhu
164 - 175
10.3233/FAIA220150
Research Article
Frontiers in Artificial Intelligence and Applications
Volume 353: Computational Models of Argument
Abstract

Today AI systems are rarely made without Machine Learning (ML) and this inspires us to explore what aptly called composite argumentation systems with ML components. Concretely, against two theoretical backdrops of PABA (Probabilistic Assumption-based Argumentation) and DST (Dempster-Shafer Theory), we present a framework for such systems called c-PABA. It is argued that c-PABA lends itself to a development tool as well and to demonstrate we show that DST-based ML classifier combination and multi-source data fusion can be implemented as simple c-PABA frameworks.

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