Exploring Deep Learning for Relational Domains A Case Study with Michalski Trains
Michael Groß (MA AI)
Donnerstag, 13.9.18, 16:00 Uhr, WE5/05.013
In this master thesis the ability of deep neural networks to learn
basic relations with as less prior knowledge as possible is observed.
For this, the relational domain of the Michalski trains is used, due to
its versatility in presentation, complexity in possible classification
rules, and easy understandability. Therefore, a convolutional neuronal
network was implemented to let it learn the relations from pictures of
the trains, which separates the needed knowledge of the domain, i.e. the
size of the image, from the semantics of its relations. The empirical
results show, that the used CNN is well able to classify the trains
correctly, but also that the difficulty to learn the rule may be solely
attributed to the number of patterns needed for classifying a train
correctly, but not its logical complexity.