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

Kido, Hiroyuki and Zenker, Frank LU orcid (2017) 20th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2017 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10621 LNAI. p.523-532
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... (More)

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.

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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
PRIMA 2017 : Principles and Practice of Multi-Agent Systems - 20th International Conference, Proceedings - Principles and Practice of Multi-Agent Systems - 20th International Conference, Proceedings
series title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume
10621 LNAI
pages
10 pages
publisher
Springer
conference name
20th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2017
conference location
Nice, France
conference dates
2017-10-30 - 2017-11-03
external identifiers
  • scopus:85034245225
ISSN
0302-9743
1611-3349
ISBN
9783319691305
DOI
10.1007/978-3-319-69131-2_35
language
English
LU publication?
yes
id
59a63df3-3c04-4b56-b19a-9bd9424bf69e
date added to LUP
2017-12-11 09:06:09
date last changed
2025-01-08 02:34:39
@inproceedings{59a63df3-3c04-4b56-b19a-9bd9424bf69e,
  abstract     = {{<p>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.</p>}},
  author       = {{Kido, Hiroyuki and Zenker, Frank}},
  booktitle    = {{PRIMA 2017 : Principles and Practice of Multi-Agent Systems - 20th International Conference, Proceedings}},
  isbn         = {{9783319691305}},
  issn         = {{0302-9743}},
  language     = {{eng}},
  pages        = {{523--532}},
  publisher    = {{Springer}},
  series       = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
  title        = {{Argument-Based Bayesian Estimation of Attack Graphs : A Preliminary Empirical Analysis}},
  url          = {{http://dx.doi.org/10.1007/978-3-319-69131-2_35}},
  doi          = {{10.1007/978-3-319-69131-2_35}},
  volume       = {{10621 LNAI}},
  year         = {{2017}},
}