Abstract
When AI interacts with humans in complex environments, such as aerospace manufacturing, safety of operation is of paramount importance. Trustworthiness of AI needs to be ensured through, among other things, explainability of its behaviour and rationale, which is typically a challenge for current deep neural network-based systems.
We tackle the knowledge comprehensibility aspect of intrinsic explainability by suggesting a concept-level environment awareness model combining various complementary knowledge sources - statistical learning using dedicated property detectors through publicly available software, and crowd-sourced common-sense knowledge graphs. Our approach also addresses the issue of data-frugal learning, typical for environments with highly specific purpose-built artefacts. We adopt Gärdenfors’s Conceptual Spaces as a cognitively-motivated knowledge representation framework and apply our typicality quantification model in a use case on interpretable classification of manufacturing artefacts.
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Notes
- 1.
Cf. algorithmic and model transparency [26].
- 2.
Each of us has a unique conceptual space arising from our own experience with the world. Thus, category prototypes may also differ across individuals and so may exact meaning of symbols, whilst sill retaining necessary properties in order to ensure effective natural language communication.
- 3.
Observed as a natural kind [37] and in its natural state (i.e., not painted over or denoting an arbitrary lemon-like artefact).
- 4.
https://fastapi.tiangolo.com (accessed on 23 May 2024).
- 5.
https://cyberbotics.com (accessed on 18 February 2022).
- 6.
https://docs.omniverse.nvidia.com/isaacsim/latest (accessed on 18 March 2024).
- 7.
e.g. https://pixabay.com/photos/tool-equipment-work-craft-allen-379596 (accessed on 10 November 2023).
- 8.
https://github.com/fengsp/color-thief-py (accessed on 27 November 2023).
- 9.
http://api.conceptnet.io (accessed on 22 March 2024).
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Galetić, V., Sheldon, D., Nottle, A. (2024). Conceptual Knowledge Modelling for Human-AI Teaming in Data-Frugal Industrial Environments. In: Cabrera, I.P., Ferré, S., Obiedkov, S. (eds) Conceptual Knowledge Structures. CONCEPTS 2024. Lecture Notes in Computer Science(), vol 14914. Springer, Cham. https://doi.org/10.1007/978-3-031-67868-4_15
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