Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Binary Space Partitioning as Intrinsic Reward   An autonomous agent embodied in a humanoid robot, in order to learn from the
overwhelming flow of raw and noisy sensory, has to effectively reduce the high
spatial-temporal data dimensionality. In this paper we propose a novel method
of unsupervised feature extraction and selection with binary space
partitioning, followed by a computation of information gain that is interpreted
as intrinsic reward, then applied as immediate-reward signal for the
reinforcement-learning. The space partitioning is executed by tiny codelets
running on a simulated Turing Machine. The features are represented by concept
nodes arranged in a hierarchy, in which those of a lower level become the input
vectors of a higher level.

24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex