Uncertainty Maximization in Partially Observable Domains: A Cognitive
  Perspective
  Faced with an ever-increasing complexity of their domains of application,
artificial learning agents are now able to scale up in their ability to process
an overwhelming amount of information coming from their interaction with an
environment. However, this process of scaling does come with a cost of encoding
and processing an increasing amount of redundant information that is not
necessarily beneficial to the learning process itself. This work exploits the
properties of the learning systems defined over partially observable domains by
selectively focusing on the specific type of information that is more likely to
express the causal interaction among the transitioning states of the
environment. Adaptive masking of the observation space based on the
$\textit{temporal difference displacement}$ criterion enabled a significant
improvement in convergence of temporal difference algorithms defined over a
partially observable Markov process.
