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BEA: Building Engaging Argumentation

  • Exchanging arguments and knowledge in conversations is an intuitive way for humans to form opinions and reconcile opposing viewpoints. The vast amount of information available on the internet, often accessed through search engines, presents a considerable challenge. Managing and filtering this overwhelming wealth of data raises the potential for intellectual isolation. This can stem either from personalized searches that create “filter bubbles” by considering a user’s history and preferences, or from the intrinsic, albeit unconscious, tendency of users to seek information that aligns with their existing beliefs, forming “self-imposed filter bubbles”. To address this issue, we introduce a model aimed at engaging the user in a critical examination of presented arguments and propose the use of a virtual agent engaging in a deliberative dialogue with human users to facilitate a fair and unbiased opinion formation. Our experiments have demonstrated the success of these models and theirExchanging arguments and knowledge in conversations is an intuitive way for humans to form opinions and reconcile opposing viewpoints. The vast amount of information available on the internet, often accessed through search engines, presents a considerable challenge. Managing and filtering this overwhelming wealth of data raises the potential for intellectual isolation. This can stem either from personalized searches that create “filter bubbles” by considering a user’s history and preferences, or from the intrinsic, albeit unconscious, tendency of users to seek information that aligns with their existing beliefs, forming “self-imposed filter bubbles”. To address this issue, we introduce a model aimed at engaging the user in a critical examination of presented arguments and propose the use of a virtual agent engaging in a deliberative dialogue with human users to facilitate a fair and unbiased opinion formation. Our experiments have demonstrated the success of these models and their implementation. As a result, this work offers valuable insights for the design of future cooperative argumentative dialogue systems.show moreshow less

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Metadaten
Author:Annalena AicherORCiDGND, Klaus WeberORCiDGND, Elisabeth AndréORCiDGND, Wolfgang Minker, Stefan Ultes
URN:urn:nbn:de:bvb:384-opus4-1228428
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/122842
ISBN:9783031635359OPAC
ISBN:9783031635366OPAC
ISSN:0302-9743OPAC
ISSN:1611-3349OPAC
Parent Title (English):Robust Argumentation Machines: First International Conference, RATIO 2024, Bielefeld, Germany, June 5–7, 2024, proceedings
Publisher:Springer
Place of publication:Cham
Editor:Philipp Cimiano, Anette Frank, Michael Kohlhase, Benno Stein
Type:Conference Proceeding
Language:English
Year of first Publication:2024
Publishing Institution:Universität Augsburg
Release Date:2025/06/25
First Page:279
Last Page:295
Series:Lecture Notes in Computer Science ; 14638
DOI:https://doi.org/10.1007/978-3-031-63536-6_17
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Menschzentrierte Künstliche Intelligenz
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)