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

Towards Probabilistic Causal Discovery, Inference & Explanations for Autonomous Drones in Mine Surveying Tasks