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

  • Hiroyuki Kido
  • Frank Zenker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10621)


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.



This study was supported by JSPS KAKENHI Grant Number 15KT0041, awarded to H.K. F.Z. acknowledges funding from HANBAN, the Volkswagen Foundation (90 531), and the European Union (1225/02/03).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Logic and CognitionSun Yat-sen UniversityGuangzhouChina
  2. 2.Department of PhilosophyLund UniversityLundSweden

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