Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Context Discovery for Model Learning in Partially Observable
  Environments   The ability to learn a model is essential for the success of autonomous
agents. Unfortunately, learning a model is difficult in partially observable
environments, where latent environmental factors influence what the agent
observes. In the absence of a supervisory training signal, autonomous agents
therefore require a mechanism to autonomously discover these environmental
factors, or sensorimotor contexts.
  This paper presents a method to discover sensorimotor contexts in partially
observable environments, by constructing a hierarchical transition model. The
method is evaluated in a simulation experiment, in which a robot learns that
different rooms are characterized by different objects that are found in them.

37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
2
The categories of this paper are: math