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.…


| Author: | Annalena AicherORCiDGND, Klaus WeberORCiDGND, Elisabeth AndréORCiDGND, Wolfgang Minker, Stefan Ultes |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-1228428 |
| Frontdoor URL | https://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 |



