Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Inference and Verification of Temporal Logic
  Classifier-in-the-loop Systems   Autonomous systems embedded with machine learning modules often rely on deep
neural networks for classifying different objects of interest in the
environment or different actions or strategies to take for the system. Due to
the non-linearity and high-dimensionality of deep neural networks, the
interpretability of the autonomous systems is compromised. Besides, the machine
learning methods in autonomous systems are mostly data-intensive and lack
commonsense knowledge and reasoning that are natural to humans. In this paper,
we propose the framework of temporal logic classifier-in-the-loop systems. The
temporal logic classifiers can output different actions to take for an
autonomous system based on the environment, such that the behavior of the
autonomous system can satisfy a given temporal logic specification. Our
approach is robust and provably-correct, as we can prove that the behavior of
the autonomous system can satisfy a given temporal logic specification in the
presence of (bounded) disturbances.

27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin