Causal modelling offers great potential to provide autonomous agents the
ability to understand the data-generation process that governs their
interactions with the world. Such models capture formal knowledge as well as
probabilistic representations of noise and uncertainty typically encountered by
autonomous robots in real-world environments. Thus, causality can aid
autonomous agents in making decisions and explaining outcomes, but deploying
causality in such a manner introduces new challenges. Here we identify
challenges relating to causality in the context of a drone system operating in
a salt mine. Such environments are challenging for autonomous agents because of
the presence of confounders, non-stationarity, and a difficulty in building
complete causal models ahead of time. To address these issues, we propose a
probabilistic causal framework consisting of: causally-informed POMDP planning,
online SCM adaptation, and post-hoc counterfactual explanations. Further, we
outline planned experimentation to evaluate the framework integrated with a
drone system in simulated mine environments and on a real-world mine dataset.

部署因果模型的无人机系统在盐矿环境中遇到了混淆因素、非稳态和无法提前构建完整因果模型等挑战，为了解决这些问题，我们提出了一种基于概率因果框架的解决方案，其中包括因果知识驱动的 POMDP 规划、在线 SCM 调整和事后可反事实解释。同时，我们将计划进行实验，评估这一框架在模拟矿山环境和真实矿山数据集上与无人机系统的集成效果。