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Aggregation of Perspectives Using the Constellations Approach to Probabilistic Argumentation

Hunter, A; Noor, K; (2020) Aggregation of Perspectives Using the Constellations Approach to Probabilistic Argumentation. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence - AAAI Technical Track: Knowledge Representation and Reasoning. AAAI Press: Palo Alto, California USA. Green open access

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Abstract

In the constellations approach to probabilistic argumentation, there is a probability distribution over the subgraphs of an argument graph, and this can be used to represent the uncertainty in the structure of the argument graph. In this paper, we consider how we can construct this probability distribution from data. We provide a language for data based on perspectives (opinions) on the structure of the graph, and we introduce a framework (based on general properties and some specific proposals) for aggregating these perspectives, and as a result obtaining a probability distribution that best reflects these perspectives. This can be used in applications such as summarizing collections of online reviews and combining conflicting reports.

Type: Proceedings paper
Title: Aggregation of Perspectives Using the Constellations Approach to Probabilistic Argumentation
Event: 34th AAAI Conference on Artificial Intelligence -
Location: New York
Dates: 07 February 2020 - 12 February 2020
Open access status: An open access version is available from UCL Discovery
DOI: 10.1609/aaai.v34i03.5674
Publisher version: https://doi.org/10.1609/aaai.v34i03.5674
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.
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/10086827
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