Peter Stone's Selected Publications

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Transfer Learning and Intelligence: an Argument and Approach

Matthew E. Taylor, Gregory Kuhlmann, and Peter Stone. Transfer Learning and Intelligence: an Argument and Approach. In Proceedings of the First Conference on Artificial General Intelligence, March 2008.
AGI-2008
Google video version of the conference presentation.

Abstract

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 transfer 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.

BibTeX Entry

@InProceedings(AGI08-taylor,
author="Matthew E.\ Taylor and Gregory Kuhlmann and Peter Stone",
title="Transfer Learning and Intelligence: an Argument and Approach",
booktitle="Proceedings of the First Conference on Artificial General Intelligence",
month="March",
year="2008",
abstract="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 \emph{transfer 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.",
wwwnote={<a href="http://agi-08.org/">AGI-2008</a><br>Google video version of <a href="http://video.google.com/videoplay?docid=1984013763155542745&hl=en">the conference presentation</a>.},
)


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