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Abstraction in Argumentation: Necessary but Dangerous

While work on abstract argumentation frameworks has greatly advanced the study of argumentation in AI, its use is not without danger. One danger is that the direct modelling of examples in abstract frameworks instead of through a theory of the structure of arguments and the nature of attacks leads to ad-hoc modellings. Another danger is that it may be overlooked that abstract accounts of argumentation can implicitly make assumptions that are not shared by many of their instantiations. A variant of this is where assumptions valid for specific argumentation contexts are incorrectly generalised by abstracting away from the context. This paper gives examples of both dangers. A lesson drawn from this is that abstraction in AI research, although necessary for understanding the essentials of the object of study, can oversimplify in ways that are not easily noticed without an explicit account of the structure of arguments and the nature of attack.