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Generating instantiated argument graphs from probabilistic information

Hunter, A; (2020) Generating instantiated argument graphs from probabilistic information. In: Frontiers in Artificial Intelligence and Applications. (pp. pp. 769-776). IOS Press Green open access

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Abstract

The epistemic approach to probabilistic argumentation assigns belief to arguments. To better understand this approach, we consider structured arguments. Our approach is to start with a probability distribution, and generate an argument graph containing structured arguments with a probability assignment. We construct arguments directly from the probability distribution, rather than a knowledgebase, and then consider methods for selecting the arguments and counterarguments to present in the argument graph. This provides mechanisms for managing uncertainty in argumentation, and for argument-based explanations of probability distributions (that might come from data or from beliefs of an agent).

Type: Proceedings paper
Title: Generating instantiated argument graphs from probabilistic information
Event: ECAI 2020
Open access status: An open access version is available from UCL Discovery
DOI: 10.3233/FAIA200165
Publisher version: https://doi.org/10.3233/FAIA200165
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
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/10112069
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