Argumentation and Machine Learning

TL;DR

This paper reviews the intersection of Computational Argumentation and Machine Learning, categorizing approaches, analyzing their interactions, and discussing challenges to enhance their integration in AI.

Contribution

It systematically classifies and evaluates the types of interactions between Argumentation and Machine Learning, providing insights into their effective combination.

Findings

Identified three interaction types: synergistic, segmented, approximated.

Analyzed the suitability of argumentation forms for machine learning tasks.

Discussed limitations and future challenges in integrating these fields.

Abstract

This chapter provides an overview of research works that present approaches with some degree of cross-fertilisation between Computational Argumentation and Machine Learning. Our review of the literature identified two broad themes representing the purpose of the interaction between these two areas: argumentation for machine learning and machine learning for argumentation. Across these two themes, we systematically evaluate the spectrum of works across various dimensions, including the type of learning and the form of argumentation framework used. Further, we identify three types of interaction between these two areas: synergistic approaches, where the Argumentation and Machine Learning components are tightly integrated; segmented approaches, where the two are interleaved such that the outputs of one are the inputs of the other; and approximated approaches, where one component shadows the other at a chosen level of detail. We draw conclusions about the suitability of certain forms of Argumentation for supporting certain types of Machine Learning, and vice versa, with clear patterns emerging from the review. Whilst the reviewed works provide inspiration for successfully combining the two fields of research, we also identify and discuss limitations and challenges that ought to be addressed in order to ensure that they remain a fruitful pairing as AI advances.