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Empirical Methods for Modelling Persuadees in Dialogical Argumentation

Hunter, A; Polberg, S; (2017) Empirical Methods for Modelling Persuadees in Dialogical Argumentation. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). (pp. pp. 382-389). IEEE Green open access

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Abstract

For a participant to play persuasive arguments in a dialogue, s/he may create a model of the other participants. This may include an estimation of what arguments the other participants find believable, convincing, or appealing. The participant can then choose to put forward those arguments that have high scores in the desired criteria. In this paper, we consider how we can crowd-source opinions on the believability, convincingness, and appeal of arguments, and how we can use this information to predict opinions for specific participants on the believability, convincingness, and appeal of specific arguments. We evaluate our approach by crowd-sourcing opinions from 50 participants about 30 arguments. We also discuss how this form of user modelling can be used in a decision-theoretic approach to choosing moves in dialogical argumentation.

Type: Proceedings paper
Title: Empirical Methods for Modelling Persuadees in Dialogical Argumentation
Event: IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), 6-8 Nov. 2017, Boston, MA, USA
Location: Boston, MA
Dates: 06 November 2017 - 08 November 2017
ISBN-13: 978-1-5386-3876-7
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICTAI.2017.00066
Publisher version: https://doi.org/10.1109/ICTAI.2017.00066
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Computational modeling, Probabilistic logic, Correlation, Vaccines, Immune system, Diseases, dialogical argumentation, opponent modelling
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10058905
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