  We investigate the use of attentional neural network layers in order to learn
a `behavior characterization' which can be used to drive novelty search and
curiosity-based policies. The space is structured towards answering a
particular distribution of questions, which are used in a supervised way to
train the attentional neural network. We find that in a 2d exploration task,
the structure of the space successfully encodes local sensory-motor
contingencies such that even a greedy local `do the most novel action' policy
with no reinforcement learning or evolution can explore the space quickly. We
also apply this to a high/low number guessing game task, and find that guessing
according to the learned attention profile performs active inference and can
discover the correct number more quickly than an exact but passive approach.
