  Current discussions of "Explainable AI" (XAI) do not much consider the role
of abduction in explanatory reasoning (see Mueller, et al., 2018). It might be
worthwhile to pursue this, to develop intelligent systems that allow for the
observation and analysis of abductive reasoning and the assessment of abductive
reasoning as a learnable skill. Abductive inference has been defined in many
ways. For example, it has been defined as the achievement of insight. Most
often abduction is taken as a single, punctuated act of syllogistic reasoning,
like making a deductive or inductive inference from given premises. In
contrast, the originator of the concept of abduction---the American
scientist/philosopher Charles Sanders Peirce---regarded abduction as an
exploratory activity. In this regard, Peirce's insights about reasoning align
with conclusions from modern psychological research. Since abduction is often
defined as "inferring the best explanation," the challenge of implementing
abductive reasoning and the challenge of automating the explanation process are
closely linked. We explore these linkages in this report. This analysis
provides a theoretical framework for understanding what the XAI researchers are
already doing, it explains why some XAI projects are succeeding (or might
succeed), and it leads to design advice.
