neural networks research group
areas
people
projects
demos
publications
software/data
Transfer Learning and Intelligence: an Argument and Approach (2008)
Matthew E. Taylor
and
Gregory Kuhlmann
and
Peter Stone
In order to claim fully general intelligence in an autonomous agent, the ability to learn is one of the most central capabilities. Classical machine learning techniques have had many significant empirical successes, but large real-world problems that are of interest to generally intelligent agents require learning much faster (with much less training experience) than is currently possible. This paper presents emphtransfer learning, where knowledge from a learned task can be used to significantly speed up learning in a novel task, as the key to achieving the learning capabilities necessary for general intelligence. In addition to motivating the need for transfer learning in an intelligent agent, we introduce a novel method for selecting types of tasks to be used for transfer and empirically demonstrate that such a selection can lead to significant increases in training speed in a two-player game.
View:
PDF
,
PS
,
HTML
Citation:
In
Proceedings of the First Conference on Artificial General Intelligence
, March 2008.
Bibtex:
@InProceedings{AGI08-taylor, title={Transfer Learning and Intelligence: an Argument and Approach}, author={Matthew E. Taylor and Gregory Kuhlmann and Peter Stone}, booktitle={Proceedings of the First Conference on Artificial General Intelligence}, month={March}, url="http://nn.cs.utexas.edu/?AGI08-taylor", year={2008} }
People
Gregory Kuhlmann
kuhlmann [at] cs utexas edu
Peter Stone
pstone [at] cs utexas edu
Matthew Taylor
taylorm [at] eecs wsu edu
Areas of Interest
Transfer Learning
Other Areas