Implicit Learning of Compiled Macro-Actions for Planning
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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.
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
© 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.
Mathematical Sciences not elsewhere classified