Framework for Computational Persuasion

计算说服框架

• 批准号: EP/N008294/1
• 负责人: Anthony Hunter
• 金额: $ 70.93万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN008294%2F1
• 关键词: Framework; Computational; Persuasion

Persuasion is an activity that involves one party trying to induce another party to believe something or to do something. It is an important and multifaceted human facility. Obviously, sales and marketing is heavily dependent on persuasion. But many other activities involve persuasion such as a doctor persuading a patient to drink less alcohol, a road safety expert persuading drivers to not text while driving, or an online safety expert persuading users of social media sites to not reveal too much personal information online. As computing becomes involved in every sphere of life, so too is persuasion a target for applying computer-based solutions.Many of the current persuasion technologies for behaviour change (e.g. for encouraging healthier life styles) are based on some combination of questionnaires for finding out information from users, provision of information for directing the users to better behaviour, computer games to enable users to explore different scenario concerning their behaviour, provision of diaries for getting users to record ongoing behaviour, and messages to remind the user to continue with the better behaviour.Interestingly, argumentation is not central to the current manifestations of persuasion technologies. The arguments for good behaviour seem either to be assumed before the user accesses the persuasion technology (e.g. when using diaries, or receiving email reminders), or arguments are provided implicitly in the persuasion technology (e.g. through provision of information, or through game playing). So explicit consideration of arguments and counterarguments are not supported with existing persuasion technologies. Yet in real-world persuasion, in particular in applications such as behaviour change, presenting convincing arguments, and presenting counterarguments to the user's arguments, is critically important. For example, for a doctor to persuade a patient to drink less alcohol, the doctor has to give good arguments why it is better for the patient to drink less, and for how it is possible. In this project, we intend to bring argumentation into a new generation of persuasion technologies. An automated persuasion system (APS) is a system that can engage in a dialogue with a user (the persuadee) in order to persuade the persuadee to do (or not do) some action or to believe (or not believe) something. To do this, an APS aims to use convincing arguments in order to persuade the persuadee. The dialogue may involve moves including queries, claims, and importantly, arguments that are presented according to some protocol. The dialogue may be asymmetric since the kinds of moves that the APS can present may be different to the moves that the persuadee may make. For instance, the persuadee might be restricted to only making arguments by selecting them from a menu (in order to obviate the need for natural language processing of arguments being entered). In the extreme, it may be that only the APS can make moves. Whether an argument is convincing depends on the context and on the characteristics of the persuadee. An APS maintains a model of the persuadee, and this is harnessed by the strategy of the APS in order to choose good moves to make in the dialogue.Computational persuasion is the study of formal models of dialogues involving arguments andcounterarguments, of user models, and strategies, for APSs. The overall goal of this project is to develop a formal framework for computational persuasion. This framework will extend recent developments in computational models of argument. The emphasis will be on APSs that will help users in changing behaviour (e.g. to persuade the user to drink less, or to not text while driving).

说服是一种活动，涉及一方试图诱导另一方相信某事或做某事。它是一个重要的、多方面的人类设施。很明显，销售和营销在很大程度上依赖于说服。但许多其他活动涉及说服，如医生说服病人少喝酒，道路安全专家说服司机不要在开车时发短信，或在线安全专家说服社交媒体网站的用户不要在网上透露太多的个人信息。随着计算机技术在生活的各个领域的广泛应用，说服也成为应用计算机解决方案的目标。（例如，为了鼓励更健康的生活方式）是基于调查表的某种组合，以从用户那里找出信息，提供信息以指导用户更好的行为，电脑游戏，使用户能够探索不同的情况下，他们的行为，提供日记，让用户记录正在进行的行为，和消息，以提醒用户继续与更好的行为。有趣的是，论证是不是中央目前的表现形式的说服技术。良好行为的论据似乎是在用户访问说服技术之前假设的（例如，当使用日记或接收电子邮件提醒时），或者论据在说服技术中隐含地提供（例如，通过提供信息或通过玩游戏）。因此，现有的说服技术不支持对论点和反论点的明确考虑。然而，在现实世界的说服，特别是在应用程序，如行为改变，提出令人信服的论点，并提出反驳用户的论点，是至关重要的。例如，对于一个医生来说，要说服病人少喝酒，医生必须给出好的论据，为什么病人少喝酒更好，以及如何做到这一点。在这个项目中，我们打算将论证引入新一代的说服技术。自动说服系统（APS）是一种可以与用户（被说服者）进行对话的系统，以便说服被说服者做（或不做）一些动作或相信（或不相信）某事。要做到这一点，APS的目的是使用令人信服的论点，以说服被说服者。对话可能涉及包括查询，索赔，以及重要的是，根据某些协议提出的论点。对话可能是不对称的，因为APS可以呈现的动作类型可能与被说服者可能做出的动作不同。例如，被说服者可能被限制为只能通过从菜单中选择参数来提出参数（为了避免对输入的参数进行自然语言处理的需要）。在极端情况下，可能只有APS才能采取行动。一个论点是否令人信服取决于上下文和被说服者的特点。APS维护被说服者的模型，APS的策略利用这个模型来选择对话中的好动作。计算说服是研究涉及论点和反论点的对话的正式模型，APS的用户模型和策略。这个项目的总体目标是开发一个计算说服的正式框架。这个框架将扩展最近的发展计算模型的论点。重点将放在帮助用户改变行为的APS上（例如，说服用户少喝酒，或者在开车时不要发短信）。

项目成果

Foundations for a logic of arguments

论证逻辑的基础

• DOI: 10.1080/11663081.2018.1439356
• 发表时间: 2018
• 期刊: Journal of Applied Non-Classical Logics
• 作者: Amgoud L

From psychological persuasion to abstract argumentation: A step forward

从心理说服到抽象论证：向前迈出了一步

• 发表时间: 2018
• 期刊: Proceedings of AISB Annual Convention 2018
• 作者: Corrégé J.-B.

Towards Computational Persuasion via Natural Language Argumentation Dialogues

通过自然语言论证对话实现计算说服

• 发表时间: 2019
• 作者: A Hunter

Polynomial-time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation

概率认知论证片段中认知状态的多项式时间更新

• 发表时间: 2019
• 作者: Anthony Hunter

Domain Modelling in Computational Persuasion for Behaviour Change in Healthcare

医疗保健行为改变的计算说服领域建模

• DOI: 10.48550/arxiv.1802.10054
• 发表时间: 2018
• 作者: Chalaguine L