Argument-Based Bayesian Estimation of Attack Graphs: A Preliminary Empirical Analysis
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).
- 3.Kido, H., Okamoto, K.: A Bayesian approach to argument-based reasoning for attack estimation. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 249–255 (2017)Google Scholar
- 4.Lawrence, J., Reed, C.: Argument mining using argumentation scheme structures. In: Proceedings of the 6th International Conference on Computational Models of Argument, pp. 379–390 (2016)Google Scholar
- 5.Moens, M.F.: Argumentation mining: Where are we now, where do we want to be and how do we get there? In: Proceedings of the 5th Forum on Information Retrieval Evaluation (2013)Google Scholar
- 6.Niskanen, A., Wallner, J.P., Järvisalo, M.: Synthesizing argumentation frameworks from examples. In: Proceesings of the 22nd European Conference on Artificial Intelligence, pp. 551–559 (2016)Google Scholar
- 7.Palau, R.M., Moens, M.F.: Argumentation mining: the detection, classification and structure of arguments in text. In: Proceedings of the 12th International Conference on Artificial Intelligence and Law, pp. 98–107 (2009)Google Scholar
- 8.Riveret, R., Governatori, G.: On learning attacks in probabilistic abstract argumentation. In: Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems, pp. 653–661 (2016)Google Scholar