Noor, K;
Hunter, A;
(2020)
A Bayesian Probabilistic Argumentation Framework for Learning from Online Reviews.
In:
2020 IEEE 32nd International Conference on Tools with Artificial Intelligence.
(pp. pp. 742-747).
IEEE
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Abstract
In the real world it is common for agents to posit arguments concerning an issue but not directly specify the attack relations between them. Nonetheless the agent may have these attacks in mind and instead they may provide a proxy indicator through which one can infer the agent's intended argument graph (arguments and attacks). Consider online reviews, where reviews are collections of arguments for and against the product (positive and negative) under review and the rating indicates whether the positive or negative arguments succeed ultimately. In previous work [1] we have proposed a method that formalises this intuition and uses the constellations approach to probabilistic argumentation to construct a probability distribution over the set of arguments graphs the agent may have had in mind. In this paper we extend this proposal and provide a method, that uses Bayesian inference, to update the initial probability distribution using real data. We evaluate our proposal by conducting a number of simulations using synthetic data.
| Type: | Proceedings paper |
|---|---|
| Title: | A Bayesian Probabilistic Argumentation Framework for Learning from Online Reviews |
| ISBN-13: | 9781728192284 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICTAI50040.2020.00118 |
| Publisher version: | https://dx.doi.org/10.1109/ICTAI50040.2020.00118 |
| 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 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/10122488 |
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