| dc.description.abstract | Research into Artificial Intelligence has sought to create a human-like machine,
referred to as Artificial General Intelligence (AGI). Reinforcement Learning (RL),
Artificial Neural Networks, the more recent Deep and Convolutional Neural Networks
and, for this thesis most importantly, Imitation Learning (IL) have all been developed
for this purpose. But until now, recent AGI-research, e.g. Google DeepMind, underlines
Reinforcement Learning as the key to developing AGI, as this is how biological systems
learn as well. This thesis, however, argues that this current paradigm unjustifiably
neglects IL for its inferior specific task performance. This literature study illustrates the
latest advancements in RL, then compares these systems with pure IL and mixed
approaches. Next is a discussion on the essentiality of IL in our only functional example
of AGI: the human mind. Finally, all chapters are brought together in a comparison
between artificial and human IL, and it ends on a brief summary of how IL currently
contributes to RL-systems in the field of AI. | |
