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所依赖的数据生成过程的巨大潜力。这类模型不仅捕捉形式化知识，还包含自主机器人在真实环境中常遇到的噪声与不确定性的概率表示。因此，因果关系可以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性也带来了新的挑战。本文以在盐矿中运行的无人机系统为背景，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极具挑战性，原因在于混杂因素、非平稳性以及难以预先构建完整因果模型。针对这些问题，我们提出了一种概率因果框架，包括：基于因果信息的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，旨在模拟矿井环境及真实矿井数据集中集成该框架与无人机系统进行性能评估。