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Accelerating Search with Transferred Heuristics.
Matthew E. Taylor,
Gregory Kuhlmann, and Peter
Stone.
In ICAPS-07 workshop on AI Planning and Learning, September 2007.
ICAPS
2007 workshop on AI Planning and Learning
[PDF]139.9kB [postscript]215.4kB
A common goal for transfer learning research is to show that a learner can solve a source task and then leverage the learned knowledge to solve a target task faster than if it had learned the target task directly. A more difficult goal is to reduce the total training time so that learning the source task and target task is faster than learning only the target task. This paper addresses the second goal by proposing a transfer hierarchy for 2-player games. Such a hierarchy orders games in terms of relative solution difficulty and can be used to select source tasks that are faster to learn than a given target task. We empirically test transfer between two types of tasks in the General Game Playing domain, the testbed for an international competition developed at Stanford. Our results show that transferring learned search heuristics from tasks in different parts of the hierarchy can significantly speed up search even when the source and target tasks differ along a number of important dimensions.
@inproceedings(ICAPS07WS-taylor,
author="Matthew E.\ Taylor and Gregory Kuhlmann and Peter Stone",
title="Accelerating Search with Transferred Heuristics",
Booktitle="{ICAPS}-07 workshop on AI Planning and Learning",
month="September",year="2007",
abstract={A common goal for transfer learning research is to show that a learner can solve a source task and then leverage the learned knowledge to solve a target task faster than if it had learned the target task directly. A more difficult goal is to reduce the total training time so that learning the source task and target task is faster than learning only the target task. This paper addresses the second goal by proposing a transfer hierarchy for 2-player games. Such a hierarchy orders games in terms of relative solution difficulty and can be used to select source tasks that are faster to learn than a given target task. We empirically test transfer between two types of tasks in the General Game Playing domain, the testbed for an international competition developed at Stanford. Our results show that transferring learned search heuristics from tasks in different parts of the hierarchy can significantly speed up search even when the source and target tasks differ along a number of important dimensions.},
wwwnote={<a href="http://www.cs.umd.edu/users/ukuter/icaps07aipl/">ICAPS 2007 workshop on AI Planning and Learning</a>},
)
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