Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Safe Learning for Uncertainty-Aware Planning via Interval MDP
  Abstraction   We study the problem of refining satisfiability bounds for partially-known
stochastic systems against planning specifications defined using syntactically
co-safe Linear Temporal Logic (scLTL). We propose an abstraction-based approach
that iteratively generates high-confidence Interval Markov Decision Process
(IMDP) abstractions of the system from high-confidence bounds on the unknown
component of the dynamics obtained via Gaussian process regression. In
particular, we develop a synthesis strategy to sample the unknown dynamics by
finding paths which avoid specification-violating states using a product IMDP.
We further provide a heuristic to choose among various candidate paths to
maximize the information gain. Finally, we propose an iterative algorithm to
synthesize a satisfying control policy for the product IMDP system. We
demonstrate our work with a case study on mobile robot navigation.

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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: physics
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The categories of this paper are: ao-sci
18
The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-ex