Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Map Classification using Local Observations   We consider the problem of classifying a map using a team of communicating
robots. It is assumed that all robots have localized visual sensing
capabilities and can exchange their information with neighboring robots. Using
a graph decomposition technique, we proposed an offline learning structure that
makes every robot capable of communicating with and fusing information from its
neighbors to plan its next move towards the most informative parts of the
environment for map classification purposes. The main idea is to decompose a
given undirected graph into a union of directed star graphs and train robots
w.r.t a bounded number of star graphs. This will significantly reduce the
computational cost of offline training and makes learning scalable (independent
of the number of robots). Our approach is particularly useful for fast map
classification in large environments using a large number of communicating
robots. We validate the usefulness of our proposed methodology through
extensive simulations.

33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg