 Besides abstract argumentation, three other important lines of research in AI are: the (internal) structure of arguments; argumentation in multi-agent systems; applications to specific tasks and domains (Rahwan &amp; Siwari 2009). The structural approach investigates formally features such as argument strength/force (e.g., a conclusive argument is stronger than a defeasible argument), argument schemes (Bex, Prakken, Reed, &amp; Walton 2003) etc. Argumentation in multi-agent systems is a thriving subfield with its own dedicated conference series (ArgMAS), based on the recognition that argumentation is a particularly suitable vehicle to facilitate interaction in the artificial environments studied by AI researchers working on multi-agent systems (see a special issue of the journal Argument &amp; Computation [Atkinson, Cerutti, et al. 2016]). Finally, computational approaches in argumentation have also thrived with respect to specific domains and applications, such as legal argumentation (Prakken &amp; Sartor 2015). Recently, as a reaction to the machine-learning paradigm, the idea of explainable AI has gotten traction, and the concept of argumentation is thought to play a fundamental role for explainable AI (Sklar &amp; Azhar 2018).