Curiosity: Emergent Behavior Through Interacting Multi-Level Predictions

Document Type

Poster Session

Publication Date


Published In

Designing For Curiosity: An Interdisciplinary Workshop


Over the past 15 years our research group has been exploring models of developmental robotics and curiosity. Our research is based on the premise that intelligent behavior arises through emergent interactions between opposing forces in an open-ended, task-independent environment. In an initial experiment we constructed a recurrent neural network model where self-motivation was "an emergent property generated by the competing pressures that arise in attempting to balance predictability and novelty". The system first focused on its error, then learned to successfully predict its error, and finally became habituated to what caused the error. This process of focusing, learning, and habituating can be seen as a rudimentary type of curiosity.


Designing For Curiosity: An Interdisciplinary Workshop

Conference Dates

May 7, 2017

Conference Location

Denver, CO