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Learning and Updating User Models for Subpopulations in Persuasive Argumentation Using Beta Distribution

Hadoux, E; Hunter, A; (2018) Learning and Updating User Models for Subpopulations in Persuasive Argumentation Using Beta Distribution. In: Andre, E and Koenig, S and Dastani, M and Sukthankar, G, (eds.) AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. (pp. pp. 1141-1149). Association for Computing Machinery (ACM): New York, NY, USA. Green open access

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Abstract

Persuasion is an activity that involves one party (the persuader) trying to induce another party (the persuadee) to believe or do something. It is an important and multifaceted human facility both in professional life (e.g., a doctor persuading a patient to give up smoking) and everyday life (e.g., some friends persuading another to join them in seeing a film). Recently, some proposals in the field of computational models of argument have been made for probabilistic models of what the persuadee knows about, or believes. However, they cannot efficiently model uncertainty on the belief of individuals and cannot represent populations. We propose to use mixtures of beta distributions and apply them on real data gathered by linguists. We show that we can represent the belief and its uncertainty using beta mixtures and that we can predict the evolution of this belief after an argument is given. We also present examples of how to use the mixtures in practice to replace general belief update functions.

Type: Proceedings paper
Title: Learning and Updating User Models for Subpopulations in Persuasive Argumentation Using Beta Distribution
Event: 17th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '18)
Open access status: An open access version is available from UCL Discovery
Publisher version: https://dl.acm.org/citation.cfm?id=3237865
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: Persuasive Argumentation, Belief Representation
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/10047347
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