EQRbot: A chatbot delivering EQR argument-based explanations.
| Autor: | Castagna F; School of Computer Science, University of Lincoln, Lincoln, United Kingdom., Garton A; School of Computer Science, University of Lincoln, Lincoln, United Kingdom., McBurney P; Department of Informatics, King's College London, London, United Kingdom., Parsons S; School of Computer Science, University of Lincoln, Lincoln, United Kingdom., Sassoon I; Department of Computer Science, Brunel University London, London, United Kingdom., Sklar EI; Lincoln Institute for Agri-Food Technology, University of Lincoln, Lincoln, United Kingdom. |
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| Jazyk: | angličtina |
| Zdroj: | Frontiers in artificial intelligence [Front Artif Intell] 2023 Mar 23; Vol. 6, pp. 1045614. Date of Electronic Publication: 2023 Mar 23 (Print Publication: 2023). |
| DOI: | 10.3389/frai.2023.1045614 |
| Abstrakt: | Recent years have witnessed the rise of several new argumentation-based support systems, especially in the healthcare industry. In the medical sector, it is imperative that the exchange of information occurs in a clear and accurate way, and this has to be reflected in any employed virtual systems. Argument Schemes and their critical questions represent well-suited formal tools for modeling such information and exchanges since they provide detailed templates for explanations to be delivered. This paper details the EQR argument scheme and deploys it to generate explanations for patients' treatment advice using a chatbot (EQRbot). The EQR scheme (devised as a pattern of Explanation-Question-Response interactions between agents) comprises multiple premises that can be interrogated to disclose additional data. The resulting explanations, obtained as instances of the employed argumentation reasoning engine and the EQR template, will then feed the conversational agent that will exhaustively convey the requested information and answers to follow-on users' queries as personalized Telegram messages. Comparisons with a previous baseline and existing argumentation-based chatbots illustrate the improvements yielded by EQRbot against similar conversational agents. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Castagna, Garton, McBurney, Parsons, Sassoon and Sklar.) |
| Databáze: | MEDLINE |
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