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

• URL: http://arxiv.org/abs/2308.10047v2
• Date: Sun, 1 Oct 2023 14:42:17 GMT
• Title: Towards Probabilistic Causal Discovery, Inference & Explanations for Autonomous Drones in Mine Surveying Tasks
• Authors: Ricardo Cannizzaro, Rhys Howard, Paulina Lewinska, Lars Kunze
• Abstract summary: Causal modelling can aid autonomous agents in making decisions and explaining outcomes. Here we identify challenges relating to causality in the context of a drone system operating in a salt mine. We propose a probabilistic causal framework consisting of: causally-informed POMDP planning, online SCM adaptation, and post-hoc counterfactual explanations.
• Score: 5.569226615350014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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