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Interpretable, Data-Efficient and Verifiable Autonomy with High-Level Knowledge
  • Zhe Xu
Zhe Xu
The University of Texas at Austin

Corresponding Author:[email protected]

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Despite the fact that artificial intelligence boosted with data-driven methods (e.g., deep neural networks) has surpassed human-level performance in various tasks, its application to autonomous
systems still faces fundamental challenges such as lack of interpretability, intensive need for data and lack of verifiability. In this overview paper, I overview some attempts to address these fundamental challenges by explaining, guiding and verifying autonomous systems, taking into account limited availability of simulated and real data, the expressivity of high-level
knowledge representations and the uncertainties of the underlying model. Specifically, this paper covers learning high-level knowledge from data for interpretable autonomous systems,
guiding autonomous systems with high-level knowledge, and
verifying and controlling autonomous systems against high-level specifications.