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.

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In ICAPS-07 workshop on AI Planning and Learning, September 2007.

@inproceedings{ICAPS07WS-taylor, title={Accelerating Search with Transferred Heuristics}, author={Matthew E. Taylor and Gregory Kuhlmann and Peter Stone}, booktitle={ICAPS-07 workshop on AI Planning and Learning}, month={September}, url="http://www.cs.utexas.edu/users/ai-lab?ICAPS07WS-taylor", year={2007} }