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.

通过自主学习的潜在事件编码，我们模拟了层次预测的发展，并提出了一种采用递归神经网络的分层体系结构，能够生成能够压缩感知动作序列的稀疏变化潜在状态。高层网络能预测潜在状态发生变化的情况。我们在仿真机器人操纵器上进行了实验，结果显示这个系统学习到了准确的数据事件结构，能够生成类似于婴儿眼追踪研究中发现的目标预期行为，并且为自主学习压缩层次编码和利用这些编码来生成自适应行为提供了新的方式。