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

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


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.


External organisations
  • Sun Yat-sen University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Probability Theory and Statistics
  • Computer Science
Original languageEnglish
Title of host publicationPRIMA 2017
Subtitle of host publicationPrinciples and Practice of Multi-Agent Systems - 20th International Conference, Proceedings
Number of pages10
Volume10621 LNAI
ISBN (Print)9783319691305
Publication statusPublished - 2017
Publication categoryResearch
Event20th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2017 - Nice, France
Duration: 2017 Oct 302017 Nov 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10621 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2017