  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.
