Extending Structural Causal Models for Autonomous Embodied Systems
<|reference_start|>Extending Structural Causal Models for Autonomous Embodied Systems: In this work we aim to bridge the divide between autonomous embodied systems and causal reasoning. Autonomous embodied systems have come to increasingly interact with humans, and in many cases may pose risks to the physical or mental well-being of those they interact with. Meanwhile causal models, despite their inherent transparency and ability to offer contrastive explanations, have found limited usage within such systems. As such, we first identify the challenges that have limited the integration of structural causal models within autonomous embodied systems. We then introduce a number of theoretical extensions to the structural causal model formalism in order to tackle these challenges. This augments these models to possess greater levels of modularisation and encapsulation, as well presenting a constant space temporal causal model representation. While not an extension itself, we also prove through the extensions we have introduced that dynamically mutable sets can be captured within structural causal models while maintaining a form of causal stationarity. Finally we introduce two case study architectures demonstrating the application of these extensions along with a discussion of where these extensions could be utilised in future work.<|reference_end|>
@article{howard2024extending,
    title={Extending Structural Causal Models for Autonomous Embodied Systems},
    author={Rhys Howard, Lars Kunze},
    journal={arXiv preprint arXiv:2406.01384},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01384},
    primaryClass={cs.AI cs.RO cs.SE}
}