. We propose a novel approach to logic-based learning which
generates assumption-based argumentation (ABA) frameworks from positive and negative examples, using a given background knowledge. These
ABA frameworks can be mapped onto logic programs with negation
as failure that may be non-stratified. Whereas existing argumentationbased methods learn exceptions to general rules by interpreting the exceptions as rebuttal attacks, our approach interprets them as undercutting attacks. Our learning technique is based on the use of transformation
rules, including some adapted from logic program transformation rules
(notably folding) as well as others, such as rote learning and assumption
introduction. We present a general strategy that applies the transformation rules in a suitable order to learn stratified frameworks, and we also
propose a variant that handles the non-stratified case. We illustrate the
benefits of our approach with a number of examples, which show that,
on one hand, we are able to easily reconstruct other logic-based learning
approaches and, on the other hand, we can work out in a very simple
and natural way problems that seem to be hard for existing techniques.