  This paper presents Abduction and Argumentation as two principled forms for
reasoning, and fleshes out the fundamental role that they can play within
Machine Learning. It reviews the state-of-the-art work over the past few
decades on the link of these two reasoning forms with machine learning work,
and from this it elaborates on how the explanation-generating role of Abduction
and Argumentation makes them naturally-fitting mechanisms for the development
of Explainable Machine Learning and AI systems. Abduction contributes towards
this goal by facilitating learning through the transformation, preparation, and
homogenization of data. Argumentation, as a conservative extension of classical
deductive reasoning, offers a flexible prediction and coverage mechanism for
learning -- an associated target language for learned knowledge -- that
explicitly acknowledges the need to deal, in the context of learning, with
uncertain, incomplete and inconsistent data that are incompatible with any
classically-represented logical theory.
