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Formalising and evaluating social trust in human-robot partnerships

形式化并评估人机伙伴关系中的社会信任

基本信息

批准号：
1896701
负责人：
金额：
--
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1896701
关键词：
Formalising evaluating social trust human

项目摘要

Rapid advances in robotics and artificial intelligence have led to robots with increasing degrees of autonomy being deployed on a wide scale, to mention home assistive robots, driverless cars and robot-assisted surgery. Autonomous robots are independent decision makers and will need to interact and work in partnerships with humans, for example in semi-autonomous driving or firefighter teams that include robotic agents. It is therefore important that they understand the social and ethical context. Human partnerships are guided by trust, which is a subjective notion that evolves based on interactions between agents. Inappropriate trust in autonomous systems can lead to misuse, as demonstrated by the recent fatal Tesla crash, or disuse. Trust has been studied in a variety of contexts, including reputation or credentials based trust in e-commerce, but few works have considered social trust, which is the human notion of trust needed to form human-robot partnerships. One exception is the theoretical framework for reasoning about trust proposed in (http://qav.comlab.ox.ac.uk/bibitem.php?key=HK17), where a probabilistic temporal logic with novel cognitive and trust operators is formulated and its model checking complexity analysed. This proposal aims to develop a practical implementation of the above framework for reasoning about trust that is powerful enough to capture autonomy, subjective preferences and rational behaviour. A key feature will be evaluation of trust achieved through probabilistic model checking. The main uses of the framework will be to (i) guide human trust in robots, (ii) enable programming of robots with subjective social trust, (iii) integrate with data about dynamical interaction, and (iv) provide information about trust-based decisions that can inform analyses of accountability in case of accidents. Our project is rooted in temporal logic, which is used to express trust. In particular, we plan to revise the logi semantics to ensure it is consistent, intuitive and expressive. Evaluating trust formulas uses model checking techniques and therefore developing our tool will involve implementing model checking algorithms or integrating with existing model checkers, for example the PRISM probabilistic model checker. We also envisage a cooperation with industrial partners (such as manufacturers of robots or driverless cars) to validate our framework and investigate how it can be used in real-life scenarios. A satisfying outcome of the project would be a complete framework and its implementation, which could be used as a reference for software developers programming autonomous systems, and by humans to guide their levels of reliance on robots. We believe our proposed research fits very well with ESPRC's Cross-ICT priorities for 2017-2020. Our main objective - improving the interaction between humans and robots and providing guidance for humans on how to use autonomous systems safely - contributes not only to the People at the Heart of ICT priority, but also to Safe and Secure ICT priority, by reducing risk and unpredictability involved in human-robot interaction.

机器人技术和人工智能的快速发展，使得自主程度越来越高的机器人得到了广泛的应用，比如家庭辅助机器人、无人驾驶汽车和机器人辅助手术。自主机器人是独立的决策者，需要与人类互动和合作，例如在半自动驾驶或包括机器人代理的消防团队中。因此，让他们了解社会和伦理背景是很重要的。人类的伙伴关系是以信任为指导的，信任是一种主观的概念，它是基于行动者之间的互动而发展起来的。对自动驾驶系统的不当信任可能导致误用，比如最近发生的特斯拉致命车祸。信任已经在各种背景下进行了研究，包括电子商务中基于声誉或凭据的信任，但很少有作品考虑到社会信任，这是形成人机伙伴关系所需的人类信任概念。一个例外是（http://qav.comlab.ox.ac.uk/bibitem.php?key=HK17）中提出的关于信任推理的理论框架，其中提出了一种具有新颖认知和信任算子的概率时间逻辑，并分析了其模型检查复杂性。本提案旨在开发上述信任推理框架的实际实施，该框架足够强大，可以捕捉自主性、主观偏好和理性行为。一个关键特征将是通过概率模型检查来评估信任。该框架的主要用途将是(i)引导人类对机器人的信任，（ii）使机器人具有主观社会信任的编程，（iii）与动态交互的数据集成，以及（iv）提供有关基于信任的决策的信息，这些决策可以为事故发生时的问责分析提供信息。我们的项目植根于时间逻辑，用来表达信任。特别是，我们计划修改逻辑语义，以确保它是一致的，直观的和富有表现力的。评估信任公式使用模型检查技术，因此开发我们的工具将涉及实现模型检查算法或与现有模型检查器集成，例如PRISM概率模型检查器。我们还设想与工业合作伙伴（如机器人或无人驾驶汽车制造商）合作，以验证我们的框架，并研究如何在现实生活中使用它。该项目的一个令人满意的结果将是一个完整的框架及其实现，它可以作为软件开发人员编程自主系统的参考，并由人类来指导他们对机器人的依赖程度。我们认为，我们提出的研究非常符合ESPRC 2017-2020年跨ict优先事项。我们的主要目标-改善人与机器人之间的互动，并为人类如何安全使用自主系统提供指导-不仅有助于以人为本的ICT优先事项，而且通过减少人机交互中的风险和不可预测性，也有助于安全可靠的ICT优先事项。