Defensibility-Based Classification for Argument Mining

This paper shows a preliminary report regarding classification techniques based on argumentation theory in artificial intelligence. A classification problem is defined on a directed graph, i.e., An argumentation framework, where each node represents an argument and each edge an attack relation between connected arguments. A hypothesis space is defined by all possible argumentation consequences, i.e., Extensions. A target argument is classified as justified or overruled, according to the best extensions minimizing errors with respect to training examples, i.e., Tuples of arguments and their correct classes. We give ideal downward and upward refinement operators for calculating hypotheses step by step. Algorithm analysis and performance evaluation are future work.