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Learning arguments is highly relevant to the field of explainable artificialintelligence. It is a family of symbolic machine learning techniques that isparticularly human-interpretable. These techniques learn a set of arguments asan intermediate representation. Arguments are small rules with exceptions thatcan be chained to larger arguments for making predictions or decisions. Weinvestigate the learning of arguments, specifically the learning of argumentsfrom a 'case model' proposed by Verheij [34]. The case model in Verheij'sapproach are cases or scenarios in a legal setting. The number of cases in acase model are relatively low. Here, we investigate whether Verheij's approachcan be used for learning arguments from other types of data sets with a muchlarger number of instances. We compare the learning of arguments from a casemodel with the HeRO algorithm [15] and learning a decision tree.

Explainable AI through the Learning of Arguments