Castagna Federico, Garton Alexandra, McBurney Peter, Parsons Simon, Sassoon Isabel, Sklar Elizabeth I
School of Computer Science, University of Lincoln, Lincoln, United Kingdom.
Department of Informatics, King's College London, London, United Kingdom.
Front Artif Intell. 2023 Mar 23;6:1045614. doi: 10.3389/frai.2023.1045614. eCollection 2023.
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.
近年来,出现了几个新的基于论证的支持系统,尤其是在医疗保健行业。在医疗领域,信息的交流必须清晰准确,这一点必须体现在任何使用的虚拟系统中。论证方案及其关键问题是用于对这类信息和交流进行建模的非常合适的形式工具,因为它们为要提供的解释提供了详细的模板。本文详细介绍了EQR论证方案,并使用聊天机器人(EQRbot)将其用于生成患者治疗建议的解释。EQR方案(设计为智能体之间解释-问题-响应交互的模式)包含多个前提,可对其进行询问以披露更多数据。作为所使用的论证推理引擎和EQR模板的实例而获得的最终解释,将为对话智能体提供信息,该智能体将作为个性化的Telegram消息详尽地传达所请求的信息并回答后续用户的查询。与之前的基线和现有的基于论证的聊天机器人的比较说明了EQRbot相对于类似对话智能体所产生的改进。