Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explainable AI through the Learning of Arguments   Learning arguments is highly relevant to the field of explainable artificial
intelligence. It is a family of symbolic machine learning techniques that is
particularly human-interpretable. These techniques learn a set of arguments as
an intermediate representation. Arguments are small rules with exceptions that
can be chained to larger arguments for making predictions or decisions. We
investigate the learning of arguments, specifically the learning of arguments
from a 'case model' proposed by Verheij [34]. The case model in Verheij's
approach are cases or scenarios in a legal setting. The number of cases in a
case model are relatively low. Here, we investigate whether Verheij's approach
can be used for learning arguments from other types of data sets with a much
larger number of instances. We compare the learning of arguments from a case
model with the HeRO algorithm [15] and learning a decision tree.

23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an