A novel structured argumentation framework for improved explainability
  of classification tasks
Lucas Rizzo Luca Longo
This paper presents a novel framework for structured argumentation, named
extend argumentative decision graph ($xADG$). It is an extension of
argumentative decision graphs built upon Dung's abstract argumentation graphs.
The $xADG$ framework allows for arguments to use boolean logic operators and
multiple premises (supports) within their internal structure, resulting in more
concise argumentation graphs that may be easier for users to understand. The
study presents a methodology for construction of $xADGs$ and evaluates their
size and predictive capacity for classification tasks of varying magnitudes.
Resulting $xADGs$ achieved strong (balanced) accuracy, which was accomplished
through an input decision tree, while also reducing the average number of
supports needed to reach a conclusion. The results further indicated that it is
possible to construct plausibly understandable $xADGs$ that outperform other
techniques for building $ADGs$ in terms of predictive capacity and overall
size. In summary, the study suggests that $xADG$ represents a promising
framework to developing more concise argumentative models that can be used for
classification tasks and knowledge discovery, acquisition, and refinement.