@inProceedings{
 title = {Towards rapid multi-robot learning from demonstration at the RoboCup competition},
 type = {inProceedings},
 year = {2015},
 identifiers = {[object Object]},
 volume = {8992},
 id = {9f94f200-9076-3f45-81b9-6d21a86f57ae},
 created = {2016-08-31T18:09:21.000Z},
 file_attached = {false},
 profile_id = {47c055dc-1578-3938-9e7e-a5608f2530b3},
 last_modified = {2017-03-15T21:19:14.543Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© Springer International Publishing Switzerland 2015.We describe our previous and current efforts towards achieving an unusual personal RoboCup goal: to train a full team of robots directly through demonstration, on the field of play at the RoboCup venue, how to collaboratively play soccer, and then use this trained team in the competition itself. Using our method, HiTAB, we can train teams of collaborative agents via demonstration to perform nontrivial joint behaviors in the form of hierarchical finite-state automata. We discuss HiTAB, our previous efforts in using it in RoboCup 2011 and 2012, recent experimental work, and our current efforts for 2014, then suggest a new RoboCup Technical Challenge problem in learning from demonstration.},
 bibtype = {inProceedings},
 author = {Freelan, D. and Wicke, D. and Sullivan, K. and Luke, S.},
 booktitle = {Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)}
}

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