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\begin{document}

\title{Interpretable, Data-Efficient and Verifiable Autonomy with High-Level
Knowledge}



\author[1]{Zhe Xu }%
\affil[1]{The University of Texas at Austin}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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