Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Information-Theoretic Abstractions for Resource-Constrained Agents via
  Mixed-Integer Linear Programming   In this paper, a mixed-integer linear programming formulation for the problem
of obtaining task-relevant, multi-resolution, graph abstractions for
resource-constrained agents is presented. The formulation leverages concepts
from information-theoretic signal compression, specifically the information
bottleneck (IB) method, to pose a graph abstraction problem as an optimal
encoder search over the space of multi-resolution trees. The abstractions
emerge in a task-relevant manner as a function of agent information-processing
constraints, and are not provided to the system a priori. We detail our
formulation and show how the problem can be realized as an integer linear
program. A non-trivial numerical example is presented to demonstrate the
utility in employing our approach to obtain hierarchical tree abstractions for
resource-limited agents.

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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol