Dynamics of Causal Knowledge

我们在复杂的动态环境中运营，包括高度敏感和安全关键的情况，如医疗紧急情况，灾难管理和空中交通管制系统。我们对什么导致什么的了解在这种情况下做出正确的决定时起着关键作用。为了确保潜在的因果知识系统的鲁棒性和良好的行为，它们的设计和实现必须有正式的良好基础。这是一项重要但艰巨的挑战。该项目旨在系统地开发一个基于逻辑的框架，以充分捕捉和推理不断发展的因果知识。这项研究有望成为智能决策的基础，并在实际应用中进行评估。

We operate in complex dynamic environments including highly sensitive and safety-critical situations such as medical emergencies, disaster management and air-traffic control systems. Our knowledge of what causes what plays a pivotal role in making correct decisions in such situations. To ensure robustness and sound behaviour of the underlying causal knowledge systems, their designs and implementations must be formally well grounded. This is an important but difficult challenge. This project aims to systematically develop a logic-based framework to adequately capture and reason about evolving causal knowledge. This research is expected to form the basis for smart decision making, and be evaluated on practical applications.