Learning Assumption-based Argumentation Frameworks
This work addresses the challenge of logic-based learning for argumentation frameworks, offering a method that simplifies handling non-stratified cases, though it appears incremental in its adaptation of transformation rules.
The authors tackled the problem of learning assumption-based argumentation (ABA) frameworks from examples and background knowledge, proposing a novel approach that interprets exceptions as undercutting attacks rather than rebuttal attacks, and demonstrated its ability to reconstruct other logic-based learning methods and handle problems difficult for existing techniques.
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 argumentation-based 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.