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Human-machine learning of ambiguities to support safe, effective, and legal decision making

人机学习歧义以支持安全、有效、合法的决策

基本信息

批准号：
EP/X030156/1
负责人：
Alireza Tamaddoni-Nezhad
金额：
$ 113.14万
依托单位：
University of Surrey
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FX030156%2F1
关键词：
Human machine learning ambiguities support

项目摘要

Mobile autonomous robots offer huge potential to help humans and reduce risk to life in a variety of potentially dangerous defence and security (as well as civilian) applications. However, there is an acute lack of trust in robot autonomy in the real world - in terms of operational performance, adherence to the rules of law and safety, and human values. Furthermore, poor transparency and lack of explainability (particularly with popular deep learning methods) add to the mistrust when autonomous decisions do not align with human "common sense". All of these factors are preventing the adoption of autonomous robots and causing a barrier to the future vision of seamless human-robot cooperation. The crux of the problem is that autonomous robots do not perform well under the many types of ambiguity that arise commonly in the real world. These can be caused by inadequate sensing information or conflicting objectives of performance, safety, and legality. On the other hand, humans are very good at recognising and resolving these ambiguities.This project aims to imbue autonomous robots with a human-like ability to handle real-world ambiguities. This will be achieved through the logical and probabilistic machine learning approach of Bayesian meta-interpretive learning (BMIL). In simple terms, this approach uses a set of logical statements (i.e., propositions, connectives, etc.) that are akin to human language. In contrast, the popular approach of deep learning uses complex multi-layered neural networks with millions of numerical connections. It is through the logical reprsentation and human-like reasoning of BMIL that it will be possible to encode expert human knowledge into the perceptive "world model" and deliberative "planner" of the robot's "artificial brain". The human-like decision-making will be encoded in a variety of ways: A) By design from operational and legal experts in the form of initial logical rules; B) Through passive learning of new logical representations and rules during intervention by human overrides when the robot is not behaving as expected; and C) Through recognising ambiguities before they arise and active learning of rules to resolve them with human assistance.A general autonomy framework will be developed to incorporate the new approach. It is intended that this will be applicable to all forms of autonomous robots in all applications. However, as a credible and feasible case study, we are focusing our real-world experiments on aquatic applications using an uncrewed surface vehicle (USV) or "robot boat" with underwater acoustic sensors (sonar) for searching underwater spaces. This problem is relevant in several areas of defence and security, including water gap crossing, naval mine countermeasures, and anti-submarine warfare. Specifically, our application focus will be on the police underwater search problem, which has challenging operational goals (i.e., finding small and potentially concealed objects underwater and amidst clutter), as well as considerations for the safety of the human divers and other users of the waterway (e.g., akin to the International Regulations for Preventing Collisions at Sea), and legal obligations relating to preservation of the evidence chain and timeliness due to custodial constraints.

移动的自主机器人在各种潜在危险的国防和安全（以及民用）应用中为帮助人类并降低生命风险提供了巨大的潜力。然而，在真实的世界中，人们对机器人自主性严重缺乏信任--在操作性能、遵守法律和安全规则以及人类价值观方面。此外，透明度差和缺乏可解释性（特别是使用流行的深度学习方法）会在自主决策不符合人类“常识”时增加不信任。所有这些因素都阻碍了自主机器人的采用，并对未来人机无缝合作的愿景造成了障碍。问题的关键在于，自主机器人在真实的世界中常见的许多类型的模糊性下表现不佳。这可能是由于传感信息不足或性能、安全性和合法性目标冲突造成的。另一方面，人类非常擅长识别和解决这些模糊性。该项目旨在赋予自主机器人类似人类的能力来处理现实世界的模糊性。这将通过贝叶斯元解释学习（BMIL）的逻辑和概率机器学习方法来实现。简单地说，这种方法使用一组逻辑语句（即，命题、连接词等）类似于人类语言的语言。相比之下，流行的深度学习方法使用具有数百万个数值连接的复杂多层神经网络。正是通过BMIL的逻辑表示和类人推理，才有可能将人类专家知识编码到机器人“人工大脑”的感知“世界模型”和审议“规划者”中。类似人类的决策将以各种方式编码：A）通过操作和法律的专家以初始逻辑规则的形式进行设计; B）当机器人没有按预期行为时，通过人工干预期间对新的逻辑表示和规则的被动学习;和C）通过在歧义出现之前识别它们，并主动学习规则以在人工帮助下解决它们。新的方法。这将适用于所有应用中的所有形式的自主机器人。然而，作为一个可信和可行的案例研究，我们正在把我们的现实世界的实验集中在水上应用，使用无人水面车辆（USV）或“机器人船”与水下声学传感器（声纳）搜索水下空间。这一问题与国防和安全的几个领域有关，包括跨越水隙、海军反水雷和反潜战。具体来说，我们的应用重点将是警察水下搜索问题，这具有挑战性的业务目标（即，在水下和混乱中寻找小的和潜在隐藏的物体），以及考虑到人类潜水员和水路的其他使用者的安全（例如，类似于《国际海上避碰规则》），以及由于保管限制而与保存证据链和及时性相关的法律的义务。