Most research in learning theory deals with passive
learning. However, many real world learning problems are interactive,
and so is animal learning.
The theoretical foundations for interactive learning and behavior are
much less developed than those for passive learning. A theoretical
understanding of behavioral learning lies at the heart of a new
generation of machine intelligence, and is also at the core of many
interesting questions about adaptation and learning in biology.
Student Projects (499,
thesis projects: Theory:
We will work on
extending the existing theory to action planning. This could be worked
the scope of a Master Thesis, or a PhD Thesis.
2. Convergence in
the presence of non
- i.i.d. data
convergence of the
normalized count to the actual probability distribution rely on i.i.d.
data, as do most bounds for generalization
errors in statistical learning theory. However, in the active learning
the data are not necessarily drawn i.i.d. This project is targeted
student with an interest in mathematical statistics.
1. Control problems
This project aims at utilizing the
above mentioned theory in classical control problems. We will compare
to reinforcement learning.
What should a rat
or a robot do when
entering a new environment? We will compare theoretical predictions
with computer simulations and
learning in human behaviour
will design, and conduct, a
psychophysical experiment, focussing on a very simple learning task. We
will ask questions about the
learning behaviour of
humans. Do they chose actions in
some optimal way? Does
interaction with the world
help to learn about