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.

翻译：因果建模为自主智能体提供了理解其与世界交互过程中数据生成过程的能力，从而展现出巨大潜力。此类模型既捕获了形式化知识，又包含了自主机器人在真实环境中通常遇到的噪声与不确定性的概率表示。因此，因果关系能够辅助自主智能体做出决策并解释结果，但以这种方式部署因果关系也带来了新的挑战。本文在盐矿井运行的无人机系统背景下，识别了与因果关系相关的挑战。此类环境对自主智能体而言具有挑战性，原因在于混杂因素、非平稳性以及难以提前构建完整因果模型。为应对这些问题，我们提出了一种概率因果框架，包括：基于因果知识的POMDP规划、在线SCM自适应以及事后反事实解释。此外，我们概述了计划中的实验方案，旨在将该框架与无人机系统集成，在模拟矿井环境和真实矿井数据集上进行评估。