Argument-Based Bayesian Estimation of Attack Graphs: A Preliminary Empirical Analysis

Abstract

This paper addresses how to identify attack relations on the basis of lay arguers’ acceptability-judgments for natural language arguments. We characterize argument-based reasoning by three Bayesian network models (coherent, decisive, and positional). Each model yields a different attack relation-estimate. Subsequently, we analyze to which extent estimates are consistent with, and so could potentially predict, lay arguers’ acceptability-judgments. Evaluation of a model’s predictive ability relies on anonymous data collected online (N = 73). After applying leave-one-out cross-validation, in the best case models achieve an average area under the receiver operating curve (AUC) of.879 and an accuracy of.786. Though the number of arguments is small (N = 5), this shows that argument-based Bayesian inference can in principle estimate attack relations.