Implicit Learning of Compiled Macro-Actions for Planning

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Author(s)
Levine, John
Newton, MAHakim
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Helder Coelho, Rudi Studer, Michael Wooldridge

Date
2010
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308542 bytes

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Lisbon, Portugal

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Abstract

We build a comprehensive macro-learning system and contribute in three different dimensions that have previously not been addressed adequately. Firstly, we learn macro-sets considering implicitly the interactions between constituent macros. Secondly, we effectively learn macros that are not found in given example plans. Lastly, we improve or reduce degradation of plan-length when macros are used; note, our main objective is to achieve fast planning. Our macro-learning system significantly outperforms a very recent macro-learning method both in solution speed and plan length.

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Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence

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© 2011 IOS Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the publisher website for access to the definitive, published version.

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Mathematical Sciences not elsewhere classified

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