A Neuro-symbolic Approach to Argument Comparison in Structured Argumentation

Defeasible Logic Programming (DeLP) is a structured argumentation formalism that uses a dialectical process to decide between contradictory conclusions. Such conclusions are supported by arguments, which are compared using a comparison criterion, to decide which one prevails in conflict situations. The definition of a formal comparison criterion is a central problem in structured argumentation, which is typically assumed to be provided by the user or knowledge engineer. In this work, we propose an integration between an argumentative approach to defeasible reasoning, such as DeLP, and machine learning models. Concretely, our goal is to train a neural network to learn a comparison criterion between arguments given a training set comprised of pairs of arguments labeled with which one prevails. We conducted several experiments, using a synthetic DeLP program generator, in order to assess the performance of a neural architecture under different kinds of DeLP programs. Our results show that under specific circumstances, a comparison criterion for arguments can be successfully learned by data-driven models.