  How do cognitive agents decide what is the relevant information to learn and
how goals are selected to gain this knowledge? Cognitive agents need to be
motivated to perform any action. We discuss that emotions arise when
differences between expected and actual rates of progress towards a goal are
experienced. Therefore, the tracking of prediction error dynamics has a tight
relationship with emotions. Here, we suggest that the tracking of prediction
error dynamics allows an artificial agent to be intrinsically motivated to seek
new experiences but constrained to those that generate reducible prediction
error.We present an intrinsic motivation architecture that generates behaviors
towards self-generated and dynamic goals and that regulates goal selection and
the balance between exploitation and exploration through multi-level monitoring
of prediction error dynamics. This new architecture modulates exploration noise
and leverages computational resources according to the dynamics of the overall
performance of the learning system. Additionally, it establishes a possible
solution to the temporal dynamics of goal selection. The results of the
experiments presented here suggest that this architecture outperforms intrinsic
motivation approaches where exploratory noise and goals are fixed and a greedy
strategy is applied.
