Abstract:
Humans encounter and adapt to novel situations every day. However, adaptation
is not a trivial task to accomplish. In the field of machine learning, the statistical
underpinnings of established deep learning architectures make it difficult for these
architectures to handle certain types of novel situations. Previous research demonstrates
how computational models could better handle novel situations through indirection, an
idea inspired by the interaction between two regions of the human brain: the prefrontal
cortex and the basal ganglia. This thesis demonstrates that combining the indirection
model with deep learning methods outperforms current architectures.
