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.
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Acknowledgements
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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Kido, H., Zenker, F. (2017). Argument-Based Bayesian Estimation of Attack Graphs: A Preliminary Empirical Analysis. In: An, B., Bazzan, A., Leite, J., Villata, S., van der Torre, L. (eds) PRIMA 2017: Principles and Practice of Multi-Agent Systems. PRIMA 2017. Lecture Notes in Computer Science(), vol 10621. Springer, Cham. https://doi.org/10.1007/978-3-319-69131-2_35
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DOI: https://doi.org/10.1007/978-3-319-69131-2_35
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