BibTex format
@inproceedings{Maurizio:2022,
author = {Maurizio, P and Toni, F},
title = {Learning assumption-based argumentation frameworks},
url = {http://hdl.handle.net/10044/1/98940},
year = {2022}
}
RIS format (EndNote, RefMan)
TY - CPAPER
AB - . We propose a novel approach to logic-based learning whichgenerates assumption-based argumentation (ABA) frameworks from positive and negative examples, using a given background knowledge. TheseABA frameworks can be mapped onto logic programs with negationas 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 transformationrules, including some adapted from logic program transformation rules(notably folding) as well as others, such as rote learning and assumptionintroduction. We present a general strategy that applies the transformation rules in a suitable order to learn stratified frameworks, and we alsopropose a variant that handles the non-stratified case. We illustrate thebenefits of our approach with a number of examples, which show that,on one hand, we are able to easily reconstruct other logic-based learningapproaches and, on the other hand, we can work out in a very simpleand natural way problems that seem to be hard for existing techniques.
AU - Maurizio,P
AU - Toni,F
PY - 2022///
TI - Learning assumption-based argumentation frameworks
UR - http://hdl.handle.net/10044/1/98940
ER -