Implicit Learning of Compiled Macro-Actions for Planning

Authors:
M.A. Hakim Newton;John Levine
Affiliations:
National ICT Australia (NICTA) and IIIS, Griffith University, Australia. E-mail: mahnewton@nicta.com.au;Computer and Information Sciences, University of Strathclyde, United Kingdom. E-mail: johnl@cis.strath.ac.uk
Venue:
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Year:
2010

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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.