Show simple item record

dc.contributor.authorArkin, Ronald C.
dc.contributor.authorEndo, Yoichiro
dc.contributor.authorLee, Brian
dc.contributor.authorMacKenzie, Douglas Christopher
dc.contributor.authorMartinson, Eric
dc.date.accessioned2008-05-07T19:03:07Z
dc.date.available2008-05-07T19:03:07Z
dc.date.issued2003
dc.identifier.urihttp://hdl.handle.net/1853/21343
dc.description.abstractThis article describes three different methods for introducing machine learning into a hybrid deliberative/reactive architecture for multirobot systems: learning momentum, Q-learning, and CBR wizards. A range of simulation experiments and results are reported using the Georgia Tech MissionLab mission specification system.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectMachine learningen_US
dc.subjectMultirobot systemsen_US
dc.titleMultistrategy Learning Methods for Multirobot Systemsen_US
dc.typePaperen_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing
dc.contributor.corporatenameGeorgia Institute of Technology. Mobile Robot Laboratory


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • Mobile Robot Laboratory Publications [187]
    Papers, pre/post-prints, and presentations by faculty and students in the Georgia Tech Mobile Robot Laboratory.
  • Mobile Robot Laboratory [187]
    Papers, pre/post-prints, and presentations by faculty and students in the Georgia Tech Mobile Robot Laboratory.

Show simple item record