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

移动自主机器人在各种潜在危险的国防和安全(以及民用)应用中提供了巨大的潜力来帮助人类并降低生命风险。然而，在现实世界中，人们严重缺乏对机器人自主性的信任--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，当自主决策不符合人类的“常识”时，糟糕的透明度和缺乏可解释性(尤其是流行的深度学习方法)增加了不信任。所有这些因素都阻碍了自主机器人的采用，并对未来无缝人类与机器人合作的愿景造成了障碍。问题的症结在于，自主机器人在现实世界中常见的多种模棱两可的情况下表现不佳。这可能是由于感知信息不足或性能、安全和合法性目标相互冲突造成的。另一方面，人类非常擅长识别和解决这些歧义。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歧义。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复杂的多层神经网络，具有数百万个数字连接。正是通过BMIL的逻辑表示和类人推理，才有可能将人类的专家知识编码成机器人“人工大脑”的感知“世界模型”和深思熟虑的“规划器”。这种类似人类的决策将以各种方式编码：A)由操作和法律专家以初始逻辑规则的形式进行设计；B)在机器人行为不符合预期的情况下，通过被动学习新的逻辑表示和规则；以及C)通过在歧义出现之前识别歧义并在人工协助下主动学习规则来解决它们。将开发一个通用的自主框架来纳入新的方法。其目的是将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，我们正将我们的真实世界实验重点放在使用无人水面航行器(USV)或带有水声传感器(声纳)的“机器人船”来搜索水下空间的水上应用上。这个问题与国防和安全的几个领域有关，包括跨越水沟、海军水雷对抗和反潜战。具体地说，我们的应用重点将是警察水下搜索问题，这一问题具有挑战性的业务目标(即在水下和杂乱中寻找微小和可能隐藏的物体)，并考虑到人类潜水员和航道其他使用者的安全(例如，类似于《国际海上避碰规则》)，以及由于拘留限制而与保存证据链和及时性有关的法律义务。

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.