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Learning constraints for the epistemic graphs approach to argumentation

Hunter, A; (2020) Learning constraints for the epistemic graphs approach to argumentation. In: Computational Models of Argument. (pp. pp. 239-250). IOS Press Green open access

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Abstract

Epistemic graphs are a proposal for modelling how agents may have beliefs in arguments and how beliefs in some arguments may influence the beliefs in others. The beliefs in arguments are represented by probability distributions and influences between arguments are represented by logical constraints on these probability distributions. This allows for various kinds of influence to be represented including supporting, attacking, and mixed, and it allows for aggregation of influence to be captured, in a context-sensitive way. In this paper, we investigate methods for learning constraints, and thereby the nature of influences, from data. We evaluate our approach by showing that we can obtain constraints with reasonable quality from two publicly available studies.

Type: Proceedings paper
Title: Learning constraints for the epistemic graphs approach to argumentation
Open access status: An open access version is available from UCL Discovery
DOI: 10.3233/FAIA200508
Publisher version: https://doi.org/10.3233/FAIA200508
Language: English
Additional information: This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). See: https://creativecommons.org/licenses/by-nc/4.0/deed.en_US
UCL classification: UCL
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/10113513
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