TY - GEN
T1 - Argument-Based Bayesian Estimation of Attack Graphs
T2 - 20th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2017
AU - Kido, Hiroyuki
AU - Zenker, Frank
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-69131-2_35
DO - 10.1007/978-3-319-69131-2_35
M3 - Paper in conference proceeding
AN - SCOPUS:85034245225
SN - 9783319691305
VL - 10621 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 523
EP - 532
BT - PRIMA 2017
PB - Springer
Y2 - 30 October 2017 through 3 November 2017
ER -