Abstract
It is propounded that in order to avoid the “frame problem” or “symbol grounding
problem” and to create a way to analyze and realize human-like intelligence with higher
functions, it is not enough just to introduce deep learning, but it is significant to get
out of deeply penetrated “division into functional modules” and to take the approach of
“function emergence through end-to-end reinforcement learning.” The functions that
have been shown to emerge according to this approach in past works are summarized,
and the reason for the difficulty in the emergence of thinking that is a typical higher
function is made clear.
It is claimed that the proposed hypothesis that exploration grows towards think-
ing through learning, becomes a key to break through the difficulty. To realize that,
“reinforcement learning using a chaotic neural network” in which adding external ex-
ploration noises is not necessary is introduced. It is shown that a robot with two
wheels and a simple visual sensor can learn an obstacle avoidance task by using this
new reinforcement learning method.
