  Humans can make predictions on various time scales and hierarchical levels.
Thereby, the learning of event encodings seems to play a crucial role. In this
work we model the development of hierarchical predictions via autonomously
learned latent event codes. We present a hierarchical recurrent neural network
architecture, whose inductive learning biases foster the development of
sparsely changing latent state that compress sensorimotor sequences. A higher
level network learns to predict the situations in which the latent states tend
to change. Using a simulated robotic manipulator, we demonstrate that the
system (i) learns latent states that accurately reflect the event structure of
the data, (ii) develops meaningful temporal abstract predictions on the higher
level, and (iii) generates goal-anticipatory behavior similar to gaze behavior
found in eye-tracking studies with infants. The architecture offers a step
towards the autonomous learning of compressed hierarchical encodings of
gathered experiences and the exploitation of these encodings to generate
adaptive behavior.
